Enhancing docosahexaenoic acid production from Schizochytrium sp. By using waste Pichia pastoris as nitrogen source based on two-stage feeding control

To reduce the cost of docosahexaenoic acid (DHA) production from Schizochytrium sp., the waste Pichia pastoris was successfully used as an alternative nitrogen source to achieve high-density cultivation during the cell growth phase. However, due to the high oxygen consumption feature when implementing high-density cultivation, the control of both the nitrogen source and dissolved oxygen concentration (DO) at each sufficient level was impossible; thus, two realistic control strategies, including "DO sufficiency-nitrogen limitation" and "DO limitation-nitrogen sufficiency", were proposed. When using the strategy of "DO sufficiency-nitrogen limitation", the lowest maintenance coefficient of glucose (12.3 mg/g/h vs. 17.0 mg/g/h) and the highest activities of related enzymes in DHA biosynthetic routes were simultaneously obtained; thus, a maximum DHA concentration of 12.8 ± 1.2 g/L was achieved, which was 1.58-fold greater than that of the control group. Overall, two-stage feeding control for alternative nitrogen sources is an efficient strategy to industrial DHA fermentation.

Development and application of control concepts for twin-screw wet granulation in the ConsiGmaTM-25, Part 1: Granule composition

Continuous manufacturing of pharmaceuticals offers several benefits, such as increased production efficiency, enhanced product quality control, and lower environmental footprint. To fully exploit these benefits, standard operation mode (production processes with no or minimal disturbance mitigation measures) should be supported by adopting novel quality-by-control (QbC) methodologies. The paper at hand is the first part of a study focused on developing QbC algorithms for optimizing twin-screw wet granulation in the industrial manufacturing line ConsiGmaTM-25, specifically addressing granule composition. This work relieson previously established process-analytical-technology (PAT) equipment for real-time monitoring of the granule composition, i.e., the active pharmaceutical ingredient (API) and liquid content in wet granules. The developed control platform integrates model-based process control algorithms that aim to keep the API- and liquid content at target values through real-time adjustments of the process parameters. Furthermore, the platform integrates a digital operator assistant, which aims to detect and classify granulation disturbances and provides messages and instructions for the plant operator. The present manuscript systematically outlines all design steps from the development phase in the simulation environment to the final real system application and validation. The control platform's performance is demonstrated through selected test scenarios on the ConsiGmaTM-25 manufacturing line. The obtained results indicate improved disturbance robustness and an increase in intermediate/final product quality (compared to conventional operating modes): The process control algorithms successfully maintained the API- and liquid content at target values despite process disturbances. Furthermore, realistic disturbances (feeder, pump, and material) were accurately detected and classified by the digital assistant algorithm. The information was provided through a user interface, offering real-time support for plant personnel.

Evaluation of single-use optical and electrochemical pH sensors in upstream bioprocessing

Culture pH is a critical process parameter during CHO cell bioreactor operations that is key for proper cell growth, protein production, and maintaining the critical quality attributes of a monoclonal antibody drug substance. The traditional means of measuring pH in bioreactors is with an electrochemical probe that can withstand and maintain accuracy through repeated sterilization cycles. An alternative technique for measuring pH is an optical sensor composed of a fluorescent dye that is sensitive to the hydrogen ion concentration. In this work we explore single-use electrochemical and single-use optical pH sensors in stirred-tank and rocking bioreactors, respectively, to understand how their overall performance compares to traditional electrochemical probes in benchtop glass stirred tank bioreactors. We found that the single-use optical pH sensors were generally less accurate than the electrochemical probes, especially in detecting large pH drifts from the setpoint. The single-use electrochemical probes were increasingly accurate as pH was increased from <7.0 to 7.5 but tended to decrease in accuracy as the batch age increased. In conclusion, single-use pH sensors offer a convenient means to measure pH during an upstream bioprocess, but the limitations of these sensors should be built into process control such that deviations in process pH, and consequently potential fluctuations in product quality, can be avoided.

Design space determination of pharmaceutical processes: Effects of control strategies and uncertainty

The identification of process Design Space (DS) is of high interest in highly regulated industrial sectors, such as pharmaceutical industry, where assurance of manufacturability and product quality is key for process development and decision-making. If the process can be controlled by a set of manipulated variables, the DS can be expanded in comparison to an open-loop scenario, where there are no controls in place. Determining the benefits of control strategies may be challenging, particularly when the available model is complex and computationally expensive - which is typically the case of pharmaceutical manufacturing. In this study, we exploit surrogate-based feasibility analysis to determine whether the process satisfies all process constraints by manipulating the process inputs and reduce the effect of uncertainty. The proposed approach is successfully tested on two simulated pharmaceutical case studies of increasing complexity, i.e., considering (i) a single pharmaceutical unit operation, and (ii) a pharmaceutical manufacturing line comprised of a sequence of connected unit operations. Results demonstrate that different control actions can be effectively exploited to operate the process in a wider range of inputs and mitigate uncertainty.

Controller Design and Stability Analysis of Intensification Process using Analytical Exact Gain-Phase Margin approach

The controllable intensified process has received immense attention from researchers in order to deliver the benefit of process intensification to be operated in a desired way to provide a more sustainable process toward reduction of environmental impact and improvement of intrinsic safety and process efficiency. Despite numerous studies on gain and phase margin approach on conventional process systems, it is yet to be tested on intensified systems as evidenced by the lack of available literature, to improve the controller performance and robustness. Thus, this paper proposed the exact gain and phase margin (EGPM) through analytical method to develop suitable controller design for intensified system using Proportional-Integral-Derivative (PID) controller formulation, and it was compared to conventional Direct Synthesis methods (DS), Internal Model Control (IMC), and Industrial IMC method in terms of the performance and stability analysis. Simulation results showed that EGPM method provides good setpoint tracking and disturbance rejection as compared to DS, IMC, and Industrial IMC while retaining overall performance stability as time delay increases. The Bode Stability Criterion was used to determine the stability of the open-loop transfer function of each method and the result demonstrated decrease in stability as time delay increases for controllers designed using DS, IMC, and Industrial IMC, and hence control performance degrades. However, the proposed EGPM controller maintains the overall robustness and control performance throughout the increase of time delay and outperform other controller design methods at higher time delay with [Formula: see text] uncertainty test. Additionally, the proposed EGPM controller design method provides overall superior control performance with lower overshoot and shorter rise time compared to other controllers when process time constant is smaller in magnitude ([Formula: see text]) than the instrumentation element, which is one of the major concerns during the design of intensified controllers, resulting overall system with a higher order. The desired selection of gain margin and phase margin were suggested at 2.5 to 4 and 60 °-70 [Formula: see text], respectively, for a wide range of control conditions for intensified processes where higher instrumentation dynamic would be possible to achieve robust control as well. The proposed EGPM method controller is thought to be a more reliable design strategy for maintaining the overall robustness and performance of higher order and complex systems that are highly affected by time delay and high dynamic response of intensified processes.

A novel mechanistic modelling approach for microbial selection dynamics: Towards improved design and control of raceway reactors for purple bacteria

Purple phototrophic bacteria (PPB) show an underexplored potential for resource recovery from wastewater. Raceway reactors offer a more affordable full-scale solution on wastewater and enable useful additional aerobic processes. Current mathematical models of PPB systems provide useful mechanistic insights, but do not represent the full metabolic versatility of PPB and thus require further advancement to simulate the process for technology development and control. In this study, a new modelling approach for PPB that integrates the photoheterotrophic, and both anaerobic and aerobic chemoheterotrophic metabolic pathways through an empirical parallel metabolic growth constant was proposed. It aimed the modelling of microbial selection dynamics in competition with aerobic and anaerobic microbial community under different operational scenarios. A sensitivity analysis was carried out to identify the most influential parameters within the model and calibrate them based on experimental data. Process perturbation scenarios were simulated, which showed a good performance of the model.

Steps toward a digital twin for functional food production with increased health benefits

Lactobacillus rhamnosus (L. rhamnosus) is a commensal bacterium with health-promoting properties and with a wide range of applications within the food industry. To improve and optimize the control of L. rhamnosus biomass production in batch and fed-batch bioprocesses, this study proposes the application of artificial neural network (ANN) modelling to improve process control and monitoring, with potential future implementation as a basis for a digital twin. Three ANNs were developed using historical data from ten bioprocesses. These ANNs were designed to predict the biomass in batch bioprocesses with different media compositions, predict biomass in fed-batch bioprocesses, and predict the growth rate in fed-batch bioprocesses. The immunomodulatory effect of the L. rhamnosus samples was examined and found to elicit an anti-inflammatory response as evidenced by the inhibition of IL-6 and TNF-α secretion. Overall, the findings of this study reinforce the potential of ANN modelling for bioprocess optimization aimed at improved control for maximising the volumetric productivity of L. rhamnosus as an immunomodulatory agent with applications in the functional food industry.

Continuous manufacturing of monoclonal antibodies: Dynamic control of multiple integrated polishing chromatography steps using BioSMB

We propose a strategy for automation and control of multi-step polishing chromatography in integrated continuous manufacturing of monoclonal antibodies. The strategy is demonstrated for a multi-step polishing process consisting of cation exchange chromatography in bind-and-elute mode followed by mixed-mode chromatography in flowthrough mode. A BioSMB system with a customized Python control layer is used for automation and scheduling of both the chromatography steps. Further, the BioSMB valve manifold is leveraged for in-line conditioning between the two steps, as tight control of pH and conductivity is essential when operating with multimodal resins because even slight fluctuations in load conditions adversely affect the chromatography performance. The pH and conductivity of the load to the multimodal chromatography columns is consistent, despite the elution gradient of the preceding cation exchange chromatography step. Inputs from the BioSMB pH and conductivity sensors are used for real-time control of the 7 pumps and 240 valves to achieve in-line conditioning inside the BioSMB manifold in a fully automated manner. This is confirmed by showcasing different elution strategies in cation exchange chromatography, including linear gradient, step gradient and process deviations like tubing leakage. In all the above cases, the model was able to maintain the pH and conductivity of multimodal chromatography load within the range of 6 ± 0.1 pH and 7 ± 0.3 mS/cm conductivity. The strategy eliminates the need for using multiple BioSMB units or integrating external pumps, valves, mixers, surge tanks, or sensors between the two steps as is currently the standard approach, thus offering a simple and robust structure for integrating multiple polishing chromatography steps in continuous downstream monoclonal antibody purification trains.

To calibrate or not to calibrate, that is the question

Sensors used for control have become widespread in water resources recovery facilities during the strive for resource efficient operations. However, their accuracy is reliant on uncertain laboratory measurements, which are used for calibration and, in turn, to correct for sensor drift. At the same time, current sensor calibration practices are lacking clear theoretical understanding of how measurement uncertainties impact the final control action. The effects of a customarily, and ad hoc, applied calibration threshold are unknown, leading to the current situation where many wastewater treatment processes are controlled by measurements with unknown accuracy. To study how sensor accuracy is affected by calibration, including varying calibration thresholds, we developed a simple theoretical model with closed-form expressions based on the variance and bias in sensor and laboratory measurements. The model was then simulated to yield the results, which showed no practical gain of using a calibration threshold, apart from the situation when calibration is more time-consuming than validation. By contrast, the best accuracy was obtained when consistently executing calibration, which opposes common practice. Further, the sensor calibration error was shown to be transferred to the process, causing a similar deviation from the setpoint when the same sensor was used for control. This emphasizes the importance of minimizing laboratory measurement uncertainties during calibration, which otherwise directly impact operations. Due to these findings we strongly advice shifting mindset from considering calibration as a sequential detection and correction approach, towards an estimation approach, aiming to estimate bias magnitude and drift speed.

Automation and artificial intelligence in filamentous fungi-based bioprocesses: A review

By utilizing their powerful metabolic versatility, filamentous fungi can be utilized in bioprocesses aimed at achieving circular economy. With the current digital transformation within the biomanufacturing sector, the interest of automating fungi-based systems has intensified. The purpose of this paper was therefore to review the potentials connected to the use of automation and artificial intelligence in fungi-based systems. Automation is characterized by the substitution of manual tasks with mechanized tools. Artificial intelligence is, on the other hand, a domain within computer science that aims at designing tools and machines with the capacity to execute functions that would usually require human aptitude. Process flexibility, enhanced data reliability and increased productivity are some of the benefits of integrating automation and artificial intelligence in fungi-based bioprocesses. One of the existing gaps that requires further investigation is the use of such data-based technologies in the production of food from fungi.

Accelerating reinforcement learning with case-based model-assisted experience augmentation for process control

In the context of intelligent manufacturing in the process industry, traditional model-based optimization control methods cannot adapt to the situation of drastic changes in working conditions or operating modes. Reinforcement learning (RL) directly achieves the control objective by interacting with the environment, and has significant advantages in the presence of uncertainty since it does not require an explicit model of the operating plant. However, most RL algorithms fail to retain transfer learning capabilities in the presence of mode variation, which becomes a practical obstacle to industrial process control applications. To address these issues, we design a framework that uses local data augmentation to improve the training efficiency and transfer learning (adaptability) performance. Therefore, this paper proposes a novel RL control algorithm, CBR-MA-DDPG, organically integrating case-based reasoning (CBR), model-assisted (MA) experience augmentation, and deep deterministic policy gradient (DDPG). When the operating mode changes, CBR-MA-DDPG can quickly adapt to the varying environment and achieve the desired control performance within several training episodes. Experimental analyses on a continuous stirred tank reactor (CSTR) and an organic Rankine cycle (ORC) demonstrate the superiority of the proposed method in terms of both adaptability and control performance/robustness. The results show that the control performance of the CBR-MA-DDPG agent outperforms the conventional PI and MPC control schemes, and that it has higher training efficiency than the state-of-the-art DDPG, TD3, and PPO algorithms in transfer learning scenarios with mode shift situations.

Smart instrumental Odour Monitoring Station for the efficient odour emission management and control in wastewater treatment plants

Odour emission assessment in wastewater treatment plants (WWTP) is a key aspect that needs to be improved in the plant management to avoid complaints and guarantee a sustainable environment. The research presents a smart instrumental odour monitoring station (SiOMS) composed of an advanced instrumental odour monitoring system (IOMS) integrated with other measurement units, for the continuous characterization and measurement of the odour emissions, with the aim of managing the potential odour annoyance causes in real time, in order to avoid negative effects. The application and on-site validation procedure of the trained IOMS is discussed. Experimental studies have been conducted at a large-scale WWTP. Fingerprint analysis has been applied to analyze and identify the principal gaseous compounds responsible for the odour annoyance. The artificial neural network has been adopted to elaborate and dynamically update the odour monitoring classification and quantification models (OMMs) of the IOMS. The results highlight the usefulness of a real-time measurement and control system to provide continuous and different information to the plant operators, thus allowing the identification of the odour sources and the most appropriate mitigation actions to be implemented. The paper provides important information for WWTP operators, as well as for the regulating bodies, authorities, manufacturers and end-users of odour monitoring systems involved in environmental odour impact management.

Model-free adaptive filter to mitigate actuator wear

The present article introduces an adaptive filter of statistical basis developed for closed-loop control applications, whose goal is to reduce actuator wear while ensuring a similar control performance regarding the original closed-loop system. The main idea is to avoid the rapid change of the filtered signal when the system output has a derivative not statistically significant regarding the expected measurement noise. The adaptation law of the time constant of the filter is model-free, and the only required information is the variance of the additive noise that the measurements are subjected to. The performance of the proposed adaptive method is illustrated through a combined numerical and experimental study, in addition to its application in an operational oil plant. The results indicate that our formulation holds promise for extending the life of actuators and is easy to implement in most programmable logic controllers.

A multilayer affective computing model with evolutionary strategies reflecting decision-makers' preferences in process control

Many industrial control problems related to multi-objective optimization, such as controller parameters tuning, often require operators to perform multiple-step interactions without considering the changes of decision-makers' affective states and quantitative description of decision-makers' preferences during the interactive decision. Regarding this problem, we developed a multilayer affective computing model (MACM), including three factors: human personality, emotional space, and affective states, to demonstrate the iterative affective computing during the interactions. First, a concise model of affective computing-driven interactive decision-making was built before three submodules involved were described in detail. (1) An affective state recognition method based on facial expressions was presented, providing the basis for obtaining expert affective states during decision-making. (2) An identification method of affective parameters was given, providing an approach to describing personalized affective state-changing rules of different persons. (3) A definition of decision-makers' preferences in interactive decision-making was specified. In addition, a decision-makers' preferences mining method was developed by the MACM and an iterative learning control (ILC) strategy. Thus, we proposed affective computing-driven interactive decision-making method, which provided a simplified approach to converting the interactive decision problems based on decision-makers' preferences to decision issues based on incremental decision vector, along with assisting computers to learn from human experts and perform decision-making automatically in a general sense. Then, two typical process control cases-PI controller tuning for decoupling problem and manipulate vector optimization for batch processes were used to show the correctness and effectiveness of the contributions. Compared with other traditional optimization algorithms without affective state tracking and recognition (fuzzy control, ILC, reinforcement learning, and so on), experimental results indicated that the proposed method could achieve good performance. Finally, this study presented the efficiency and limitations of using this technique for a specific application.

Process control for improving dewatering performance of sewage sludge based on carbonaceous skeleton-assisted thermal hydrolysis

The poor dewaterability of sewage sludge is a major obstacle to its disposal and utilization. Our previous study developed a novel method of carbonaceous skeleton-assisted thermal hydrolysis to achieve good performance of sludge dewatering. This work was conducted for further improving the efficiency through investigating the effects of the properties of sludge, skeleton, and key process parameters. A dewatering model was also established based on Darcy's Law and experimental results from a self-designed computer control on-line filter press system. The experimental results showed that the water content can all be reduced by about 36% for sludge with the varying organic content from 35% to 60%. Lignocellulosic skeleton had better assistive capabilities than skeleton with high content of hemicellulose, lipid, and chitin, and the appropriate dosage was 0.2-0.5 g/g DS. Satisfied reduction of about 30% in water content can be obtained when sludge was assisted-hydrolyzed at a moderate temperature of 180 °C only within 5 min and dewatered at 0.4-1.0 MPa for 10-20 min. By using self-developed dewatering model, the filtrate mass with time under any mechanical pressure can be obtained and the theoretical value fit the actual value very well. Based on this, excellent dewatering performance can be achieved through process control of assisted thermal hydrolysis and mechanical dewatering.

Acetate-based production of itaconic acid with Corynebacterium glutamicum using an integrated pH-coupled feeding control

To date, most bio-based products of industrial biotechnology stem from sugar-based carbon sources originating from food and feed competing resources. Exemplary for bioproducts converted from glucose, the potential C5 platform chemical itaconic acid is presently produced by the filamentous fungus Aspergillus terreus. Here, an engineered strain of the industrial platform organism Corynebacterium glutamicum ATCC 13032 was used for acetate-based production of itaconic acid to overcome current production difficulties. For this purpose, C. glutamicum ICD^R453C (pEKEx2-malEcad_opt) with a mutated icd variant for reduced isocitrate dehydrogenase activity was constructed harbouring pEKEx2-malEcad_opt, that includes a cis-aconitate dehydrogenase gene originating from A. terreus. Overall, a peak volumetric productivity of 1.01 gL^-1h^-1 was achieved resulting in an itaconate titer of 29.2 g/L, by using an integrated pH-coupled acetate feeding control in a fed-batch process without base titration. The results support the high potential of acetate as alternative substrate for bioproduction.

Digitisation of metal AM for part microstructure and property control

Metal additive manufacturing, which uses a layer-by-layer approach to fabricate parts, has many potential advantages over conventional techniques, including the ability to produced complex geometries, fast new design part production, personalised production, have lower cost and produce less material waste. While these advantages make AM an attractive option for industry, determining process parameters which result in specific properties, such as the level of porosity and tensile strength, can be a long and costly endeavour. In this review, the state-of-the-art in the control of part properties in AM is examined, including the effect of microstructure on part properties. The simulation of microstructure formation via numerical simulation and machine learning is examined which can provide process quality control and has the potential to aid in rapid process optimisation via closed loop control. In-situ monitoring of the AM process, is also discussed as a route to enable first time right production in the AM process, along with the hybrid approach of AM fabrication with post-processing steps such as shock peening, heat treatment and rolling. At the end of the paper, an outlook is presented with a view towards potential avenues for further research required in the field of metal AM.

Impact of alarm management and automation on abnormal operations: A human-in-the-loop simulation study

Process control consists of complex states of performance that require a significant amount of operator attention and skill to manage. Automation and smart alarms can enhance these processes. This study investigated the effects of alarm management and automation on process control operator workload (NASA-TLX) and performance via material lost through flaring outcomes. Eleven console qualified operators participated in a human-in-the-loop, high-fidelity simulation-based training exercise. Three levels of alarm management (no rationalization, rationalization, and state-based smart alarm) and two levels of automation (no automation and automation) conditions were implemented using six scenarios. A repeated measures two-way MANOVA indicated that both alarm management schema and automation significantly affected operator workload and performance. These results indicate that state-based smart alarm management and automation schemas may assist operators in reducing workload and material lost through flare release during abnormal operating conditions.

Two-stage microbial fuel cell (MFC) and membrane bioreactor (MBR) system for enhancing wastewater treatment and resource recovery based on MFC as a biosensor

Lack of process control between the two stages of a combined microbial fuel cell-membrane bioreactor (MFC-MBR) system limits its application in wastewater treatment due to membrane fouling and high energy consumption. In this study, a two-stage MFC-MBR integrated system was established to investigate the impact of incorporating process control on petroleum refinery wastewater treatment. The results showed that chemical oxygen demand (COD) removal exhibits a linear relationship with the MFC voltage output (R² = 0.9821); therefore, the MFC was used as a biosensor to control the combined system. The removal efficiencies of COD, ammonium nitrogen (NH₄⁺-N), and total nitrogen (TN) were 96.3%, 92.4%, and 86.6%, respectively, in the MFC-MBR biosensor, whereas those in the control system were 74.7%, 71.2%, and 64.7% respectively. Furthermore，using the biosensor control system yielded a 50% reduction in the transmembrane pressure (1.01 kPa day^-1) and decreased membrane fouling in wastewater treatment. The maximum energy recovery of the biosensor system (0.00258 kWh m^-3) was five times higher than that of the control system, as determined by calculating the mass balance of the system. Thus, this study indicates that using the MFC as a biosensor for process control in an MFC-MBR system can improve overall system performance.

[Integrated care concepts and multidisciplinary process chains in a radiological context]

Modern patient-centered and cost-efficient care concepts in hospitals require the mapping of multidisciplinary process chains into clinical pathways. Clinical decision support systems and operations research methods use algorithms to classify patients into homogeneous groups and to model a complete clinical pathway for scheduling individual procedures. An improvement of the economic situation of the care facility can be achieved through improved resource utilization, reduced patient waiting times and a shortening of the length of stay. The interdisciplinary use of centrally stored interoperable information and comprehensive care management via information technology (IT) services lay the foundation for the dissolution of traditional IT system architectures in medicine and the development of flexibly integrable modern system platforms. New IT approaches such as the semantically standardized definition of procedures and resource properties, the use of clinical decision support systems and the use of service-oriented system architectures form the basis for the deep integration of radiology services into comprehensive interdisciplinary care concepts.

Predictive Microbial Modeling of Enterococcus faecium NRRL B-2354 Inactivation during Baking of a Multicomponent Low-Moisture Food

ABSTRACT

The use of baking ovens as a microbial kill step should be validated based on results of thermal inactivation models. Although traditional isothermal models may not be appropriate for these dynamic processes, they are being used by the food industry. Previous research indicates that the impact of additional process conditions, such as humidity, should be considered when validating thermal processes for the control of microbial hazards in low-moisture foods. In this study, the predictive performance of traditional and modified thermal inactivation kinetic models accounting for process humidity were assessed for predicting inactivation of Enterococcus faecium NRRL B-2354 in a multi-ingredient composite food during baking. Ingredients (milk powder, protein powder, peanut butter, and whole wheat flour) were individually inoculated to achieve ∼6 log CFU/g, equilibrated to a water activity of 0.25, and then mixed to form a cookie dough. An isothermal inactivation study was conducted for the dough to obtain traditional D- and z-values (n = 63). In a separate experiment, cookies were baked under four dynamic heating conditions: 135°C, high humidity; 135°C, low humidity; 150°C, high humidity; and 150°C, low humidity. Process humidity measurements; time-temperature profiles for the product core, surface, and bulk air; and microbial survivor ratios were collected for the four conditions at six residence times (n = 144). The traditional isothermal model had a high root mean square error (RMSE) of 856.51 log CFU/g, significantly overpredicting bacterial inactivation during the process. The modified model accounting for the dynamic time-temperature profile and process humidity data was a better predictor with an RMSE of 0.55 log CFU/g. These results indicate the importance of accounting for additional process parameters in baking inactivation models and that model performance can be improved by utilizing model parameters obtained directly from industrial-scale experimental data.

HIGHLIGHTS

Control strategies for the vertical gene transfer of quinolone ARGs in Escherichia coli through molecular modification and molecular dynamics

This study investigates the regulation of the vertical gene transfer of quinolones' antibiotic resistance genes (ARGs) through a combination of source modification and process control. In source prevention, 29 Escherichia coli (E. coli) DNA gyrase subunit A mutant proteins were constructed, the B-G mutant protein displayed the greatest reduction in binding effect (-25.98%). Based on this, a 3D-QSAR model was constructed, and LEV-2 and LEV-9 QNs derivatives were designed based on Levofloxacin (LEV), and their binding effect with B-G mutant protein was found be increased by 13.24% and 19.40%. The drug resistance mechanism of E. coli was explored based on molecular docking technology and protein hydrophobic interaction theory. Most of the amino acid resistance mutations changed from hydrophilic to lipophilic, which inhibited the binding of QNs to mutant protein A subunit, and further reduced the bactericidal effect of QNs. In process control, Huoxiang-Zhengqi, stroke-physiological saline solution (SPSS), and Lycium barbarum (L. barbarum) was found to be 164.82% higher than that of the blank control group. The purpose of this study is to provide a theoretical support for the joint regulation of QNs' ARGs in organisms and the research and development on green alternatives to QNs compounds.

Extraction of human noroviruses from leafy greens and fresh herbs using magnetic silica beads

Consumption of leafy greens and to a lesser extent fresh herbs has been associated with several foodborne outbreaks including human norovirus (HuNoV). However, the extraction and detection of viruses from these matrices present multiple challenges such as low recovery yields and relatively high PCR inhibition. A new magnetic silica bead based (MSB) extraction protocol was developed and used to recover norovirus from leafy greens and fresh herbs. The performance results were compared to the ISO 15216-1:2017 standard. The HuNoV GII.4 and GI.5 recovery yields from spiked lettuce using the MSB extraction protocol range from 33 to 82%. There was a good correlation between murine norovirus (MNV) and HuNoV recovery yields from fresh herbs and leafy greens. No reverse-transcriptase quantitative polymerase chain reaction (RT-qPCR) inhibition was detected from leafy green extracts using the MSB methodology. The selected commercial RT-qPCR detection kit had a major impact on RT-qPCR inhibition levels detected in the ISO 15216-1:2017 RNA extracts. RNase treatment was used to estimate genome recovery from HuNoV with intact capsids. This treatment resulted in similar HuNoV and MNV recovery yields. Between 2019 and 2020, the MSB protocol was used to conduct a survey of HuNoV in domestic and imported leafy greens and fresh herbs sold at retail in Canada. All of the 280 samples tested were negative. Overall, the use of MSB was shown to be an efficient approach to recover HuNoV from leafy greens and certain types of fresh herbs and to conduct surveys.

Improving Process Understanding in Roll Compaction

Roll compaction/ dry granulation is gaining importance. Numerous papers have been published and many attempts to model the process are available in the meantime. Johanson published a model in 1965, which is the basis for many further modifications until today. The aim of the paper is to improve process understanding in roll compaction, which can be used to setup a roll compaction process, to design a scale-up strategy or to help in process transfer between different types of roll compactors. Based on some assumptions, simple considerations help to estimate a required densification factor and to visualize the relations between roll diameter, gap width and nip angle. Two recently published papers based on simplified Johansen models are used to visualize the relations between specific compaction force and the maximal pressure experienced by the material. The influence of roll diameter, gap width and compressibility constant are discussed. This helps to estimate, if a scale-up or process transfer is reasonable. The recently introduced dimensionless Midoux-number can also be used to design scale-up or process transfer of roll compaction without knowledge about the maximal pressure. Exploring the simple concepts can help to improve process understanding even without a background in engineering.

Trade-off control of organic matter and disinfection by-products in the drinking water treatment chain: Role of pre-ozonation

Ozone is a strong oxidant commonly used in drinking water treatment, but its role in the transformation/formation of organic matters (OMs) and disinfection by-products (DBPs) in the drinking water treatment chain had not been systematically studied. In this work the occurrence and building up of OMs, DBPs of trihalomethanes (THMs) and nitrosamines (NAs) during water purification steps under different pre-ozonation dosages have been studied through lab-scale and pilot-scale studies. Results indicated that 0-0.4 mg/L of pre-ozonation dosage could reduce organic load of following-up process steps but insufficient to control DBPs. Seasonal performances of a pilot plant indicated that the accumulation of DBPs was much less in summer than in winter. Furthermore the formation potential of NAs was higher in winter than summer when 0.4 mg/L pre-ozonation was dosed while the maximum removal efficiency of organic matter was found at a pre-ozonation dosage of 0.8 mg/L in summer. Finally a seasonal trade-off control strategy for both OMs and DBPs was proposed with an elucidated role of pre-ozonation in the drinking water treatment chain. This study provided working principles on optimizing pre-ozonation dosage and a seasonal control strategy for trade-off control of both OMs and DBPs in drinking water treatment plants.

Model predictive control of azeotropic dividing wall distillation column for separating furfural-water mixture

Dividing wall distillation columns (DWDCs) are effective technologies for distillation process intensifications. Azeotropic DWDCs (ADWDCs) are effective process intensification technologies to further intensify distillation process through combination of azeotropic distillation (AD) with DWDC. Investigations on the controllability and operation ability of ADWDC are mainly employing the traditional proportional-integral (PI) control schemes. Because of the inherent complicated configuration of ADWDC, the PI control leads to relatively poor dynamic performances. So as to enhance the dynamic performances, composition control scheme employing the advanced model predictive control (MPC) for an ADWDC separating furfural and water is studied in this paper. Although there are several studies employing MPC for DWDC, few investigation has been done on using MPC for ADWDC which is more interactive than DWDC. The dynamic results obtained in this paper using MPC are compared with the corresponding PI control scheme. Although the steady-state deviations and numbers of oscillations are similar employing PI control and MPC, the settling time and maximum deviations are smaller using MPC. MPC can considerably suppress the maximum deviation in face of +20% feed flow rate disturbance from -1.449% to -0.013%, and the corresponding settling time is reduced from 4.28 h to 2.20 h. These prove that MPC is more suitable than PI control for ADWDC. This paper provides new effective control scheme for the complex and highly interactive ADWDC, and verifies that MPC is a promising method for ADWDC to provide better dynamic performances with reduced maximum deviations and shortened settling time.

Control strategy and methods for continuous direct compression processes

We presented a control strategy for tablet manufacturing processes based on continuous direct compression. The work was conducted by the experts of pharmaceutical companies, machine suppliers, academia, and regulatory authority in Japan. Among different items in the process, the component ratio and blended powder content were selected as the items requiring the control method specific to continuous manufacturing different from the conventional batch manufacturing. The control and management of the Loss in Weight (LIW) feeder were deemed the most important, and the Residence Time Distribution (RTD) model were regarded effective for setting the control range and for controlling of the LIW feeder. Based on these ideas, the concept of process control using RTD was summarized. The presented contents can serve as a solid fundament for adopting a new control method of continuous direct compression processes in and beyond the Japanese market.

Near-infrared hyperspectral imaging for monitoring the thickness distribution of thin poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) layers

The thickness of thin layers of the conductive polymer PEDOT:PSS in the range between about 60 and 300 nm was determined by a near-infrared spectroscopic method using a hyperspectral camera. The reflection spectra of the layers do not contain bands, but consist of a moderate slope of the overall reflectance in the range between 1320 and 1850 nm. Despite the low thickness, the spectra show an extremely strong dependence on the thickness of the layers, which allows their use for quantitative measurements. The prediction of quantitative thickness data from the reflection spectra was based on a chemometric approach using the partial least squares (PLS) algorithm. Calibration was carried out by means of spin-coated layers of PEDOT:PSS, whose thickness was determined by white-light interferometry and stylus profilometry. Finally, this resulted in a calibration model with a root mean square error of prediction (RMSEP) of about 9 nm. After external validation of this model, it was used for quantitative imaging of the thickness distribution in PEDOT:PSS layers. The precision of the predicted values was confirmed by comparison with data from the reference methods. Moreover, it was shown that this approach can be also used for hyperspectral imaging of the thickness of thin printed layers and structures of this conductive polymer on polymer film or paper with excellent thickness resolution. This analytical approach opens new possibilities for in-line process control by large-scale monitoring of thickness and homogeneity of thin layers of conductive polymers.

The development and validation of a short-duration sustained visual search task for process control environments

This study was designed to validate a short-duration sustained visual search task for the assessment of sustained attention in process control environments. The task consists of 10 short dynamic scenarios, which require participants (N = 121) to monitor simulated operating power transmission interfaces, and identify system faults. A vigilance decrement was demonstrated throughout the sustained visual search task, as evident in increased response latencies and decreased accuracy over time. Further, changes in response latency throughout the sustained visual search task were positively associated with changes in response latency during a 30-min, low signal probability task, a 45-min low signal probability task, and a 45-min high signal probability task. The findings indicate that the sustained visual search task may be a valid alternative to a longer-duration process control task for experimental studies, and is likely to be of value for assessments of the capacity for sustained attention of operators in process control environments.

Advanced control strategies for bioprocess chromatography: Challenges and opportunities for intensified processes and next generation products

Recent advances in process analytical technologies and modelling techniques present opportunities to improve industrial chromatography control strategies to enhance process robustness, increase productivity and move towards real-time release testing. This paper provides a critical overview of batch and continuous industrial chromatography control systems for therapeutic protein purification. Firstly, the limitations of conventional industrial fractionation control strategies using in-line UV spectroscopy and on-line HPLC are outlined. Following this, an evaluation of monitoring and control techniques showing promise within research, process development and manufacturing is provided. These novel control strategies combine rapid in-line data capture (e.g. NIR, MALS and variable pathlength UV) with enhanced process understanding obtained from mechanistic and empirical modelling techniques. Finally, a summary of the future states of industrial chromatography control systems is proposed, including strategies to control buffer formulation, product fractionation, column switching and column fouling. The implementation of these control systems improves process capabilities to fulfil product quality criteria as processes are scaled, transferred and operated, thus fast tracking the delivery of new medicines to market.

Monitoring of the residual moisture content in finished textiles during converting by NIR hyperspectral imaging

Hyperspectral imaging was used for large-scale monitoring of the residual moisture in wide textile webs at the end of the drying process that follows their washing or finishing by impregnation in aqueous solutions or dispersions. Such data are essential for optimizing the energy efficiency and the precise control of the drying process. Quantitative analysis of the recorded spectral data was carried out with multivariate regression methods such as the partial least squares (PLS) algorithm. Reference data for calibration of the prediction models were determined by gravimetry. The drying of textile materials from both natural or synthetic fibers possessing different water absorption capacities (cotton, polyamide, polyester), which were partially finished with an optical brightener, was investigated. Moisture contents in the range from 0 to about 12 wt% were considered in the calibration models. For all systems, the root mean square error of prediction (RMSEP) for the residual moisture was found to be about 0.5 wt%, that is, about 1 g/m². In addition to the quantitative determination of the water content, hyperspectral imaging provides detailed information about its spatial distribution across the textile web, which may help to improve the control of the drying process. In particular, it was demonstrated that the developed methods were capable of detecting and visualizing inhomogeneous moisture distributions. Averaging of the individual values of the moisture content predicted from all spectra across the surface of the textile samples resulted in a very close correlation with the corresponding gravimetric reference values. Due to the averaging process, the difference between both values is generally lower than RMSEP even in case of samples with inhomogeneous distribution of the moisture. The high precision and the broad capabilities of the developed analytic methods for in-line monitoring of the moisture content hold the potential for an efficient process control in technical textile converting processes.

Optimal control analysis and Practical NMPC applied to refrigeration systems

This work is focused on optimal control of mechanical compression refrigeration systems. A reduced-order state-space model based on the moving boundary approach is proposed for the canonical cycle, which eases the controller design. The optimal cycle (that satisfying the cooling demand while maximizing efficiency) is defined by three variables, but only two inputs are available, therefore the controllability of the proposed model is studied. It is shown through optimization simulations how optimal cycles for a range of the cooling demand turn out not to be achieved by keeping the degree of superheating to a minimum. The Practical NMPC and a well-known feedback-plus-feedforward strategy from the literature are compared in simulation, both showing trouble in reaching the optimal cycle, which agrees with the controllability study.

Detection of norovirus, hepatitis A and hepatitis E viruses in multicomponent foodstuffs

Among the enteric viruses implicated in foodborne outbreaks, the human norovirus and hepatitis viruses A and E (HAV and HEV) represent a serious public health concern. International standard ISO 15216 proposes methods for detecting HAV and norovirus (genogroups I and II) RNA from soft fruit, leaf, stem and bulb vegetables, bottled water or food surfaces. These methods had not previously been validated for detecting the targeted viruses in other foodstuffs such as multicomponent foods, nor for detecting other viruses in foodstuffs. The aim of this study was to characterise a method derived from the vegetable method described in ISO 15216 to detect HAV, HEV and norovirus in artificially-contaminated multicomponent foodstuffs according to the recent international standard ISO 16140-4. Results showed that the mean recovery rates for all settings did not differ according to the operator. The mean extraction yields ranged from 0.35% to 40.44% for HAV, 5.19% to 100% for HEV, 0.10% to 40.61% for norovirus GI and 0.88% to 69.16% for norovirus GII. The LOD₉₅ was 10² genome copies/g for HAV, HEV and norovirus GII and 10³ genome copies/g for norovirus GI. The LOQ was 2.90 × 10⁴, 1.40 × 10³, 1.60 × 10⁴ and 1.30 × 10⁴ genome copies/g for HAV, HEV, norovirus GI and norovirus GII respectively. The MNV-1 process control was detected in 120 out of 128 RNA extracts analysed and was recovered with an efficiency of between 3.83% and 50.22%. The mean inhibition rates of quantitative real-time RT-PCR reaction ranged from 3.25% to 28.70% and varied significantly with the type of food matrix. The described method could be used to detect viruses in composite food products for routine diagnosis needs.

Thermus thermophilus DNA can be used as internal control for process monitoring of clinical metagenomic next-generation sequencing of urine samples

INTRODUCTION

Metagenomics is increasingly considered for clinical diagnostics. In order for this technology to become integrated in the clinical microbiology laboratory, process controls are required. Molecular diagnostic tests typically integrate an internal control (IC) to detect potential sources of variation and technical failure. However, few studies report on the integration of an IC in metagenomics.

AIM

We aimed to develop an easy-to-use IC method for the process control of library preparation and sequencing applied to metagenomics in clinical microbiology diagnostics using Thermus thermophilus DNA.

METHODOLOGY

DNA was extracted from urine samples and sequenced on the Ion Torrent Proton in the absence and presence of incremental concentrations (0.5-2-5%) of IC. Between aliquots of each sample, we compared the IC relative abundance (RA), and after in silico subtraction of IC reads, analysed microbial composition and the RA of pathogens. The optimal IC concentration was defined as the lowest concentration still detectable in all samples with the smallest impact on the microbial composition.

RESULTS

The RA of IC correlated linearly with the spiked IC concentration (r² = 0.99). IC added in a concentration of 0.5% of the total DNA concentration was detectable in all sample aliquots, regardless of human-bacterial DNA proportion, and after in silico removal gave the smallest difference in RA of pathogens compared to the sample aliquot sequenced in the absence of IC. The microbial composition in the presence and absence of IC was highly similar after in silico removal of IC reads (median BC-dissimilarity per sample: 0.059), provided samples had a mean of >10,000 bacterial reads.

CONCLUSION

T. thermophilus DNA at a percentage of 0.5% of the total DNA concentration was successfully applied for the process control of metagenomics of urine samples. We demonstrated negligible alterations in sample microbial composition after in silico subtraction of IC reads. This approach contributes toward implementation of metagenomics in the clinical microbiology laboratory.

Performance evaluation of a control strategy for photosynthetic biogas upgrading in a semi-industrial scale photobioreactor

The validation of a control strategy for biogas upgrading via light-driven CO₂ consumption by microalgae and H₂S oxidation by oxidizing bacteria using the oxygen photosynthetically generated was performed in a semi-industrial scale (9.6 m³) photobioreactor. The control system was able to support CO₂ concentrations lower than 2% with O₂ contents ≤ 1% regardless of the pH in the cultivation broth (ranging from 9.05 to 9.50). Moreover, the control system was efficient to cope with variations in biogas flowrate from 143 to 420 L h^-1, resulting in a biomethane composition of CO₂ < 2.4%, CH₄ > 95.5%, O₂ < 1% and no H₂S. Despite the poor robustness of this technology against failures in biogas and liquid supply (CH₄ concentration of 67.5 and 70.9% after 2 h of biogas or liquid stoppage, respectively), the control system was capable of restoring biomethane quality in less than 2 h when biogas or liquid supply was resumed.

From powder to tablets: Investigation of residence time distributions in a continuous manufacturing process train as basis for continuous process verification

The essence of Continuous Manufacturing (CM) resides in the fact that continuous process units are directly connected to each other forming a continuous process train. The thorough understanding of material flow in this train based on suitable sensors, including on-line process analytical technologies and other sensors, is key in understanding the time-domain behavior of the system and the process. This real-time monitoring correlated with the time domain material flow behavior could be used to close control-loops. In practical terms, the implementation of such a control strategy is only feasible, if the overlying control system knows precisely what material is when and where at all times. Consequently, thorough knowledge of the residence time distribution (RTD) of the material throughout the whole manufacturing network needs to be established early on in development. Once RTD is well understood, its constant observation could also be used for continuous process verification purposes hinging on the argument that the flow pattern of the material is unchanged. As continuous processes that run over extended periods of time are susceptible to unforeseen incidents like equipment wear-out or clogging, drifts or shifts in RTD could indicate such issues early on. The presented work aims to demonstrate this proposed concept for an integrated wet-granulation CM process. To achieve this aim, three steps were completed: First, thorough RTD knowledge was generated, by inducing endogenous step-tests in active pharmaceutical ingredient (API) content in the range of ±30% at varying process conditions, and analyzing the material RTDs via NIRS analysis at four different locations in the line. Second, it was demonstrated that also low-level step tests of ±5% and even ±3% are sufficient for accurate RTD determination. This validated the possibility of continuous RTD assessment during (pre-)validation trials or even commercial manufacturing, as the drug product would comply with required quality characteristics (content uniformity, assay). In the third step, it was then demonstrated that recurring low-level step testing during routine manufacturing could be used as a way to determine the current system health, as observed changes in RTD indicated blockages and accidental material hold-up in the line. While deliberate changes in API content during commercial production might seem counter intuitive, they would actually aid in ensuring the production of quality product in a better way, than running at constant process settings over an extended period of time without the constant assessment of system health.

Review on microaeration-based anaerobic digestion: State of the art, challenges, and prospectives

Microaeration (dosing small quantities of air or oxygen) is an effective approach to facilitate anaerobic digestion (AD) process and has gained increased attention in recent years. The underlying mechanisms of the facilitation effect of microaeration on AD process were reviewed in terms of accelerating hydrolysis, scavenging hydrogen sulfide, and affecting microbial diversity. Process parameters and control strategies were summarized to reveal considerable factors in implementing microaeration-based AD process. In addition, current applications, including lab-, pilot- and full-scale level cases, were summarized to provide guidance for further improvement in large-scale applications. The challenges and future perspectives were also highlighted to promote the development of AD process associated with microaeration.

Applications of off-gas mass spectrometry in fed-batch mammalian cell culture

Off-gas analysis using a magnetic sector mass spectrometer was performed in mammalian cell cultures in the fed-batch mode at the 5 L bench and 50 L pilot scales. Factors affecting the MS gas traces were identified during the duration of the fed-batch cultures. Correlation between viable cell concentration (VCC) and oxygen concentration of the inlet gas into the bioreactor (O₂-in) resulted in R² ≈ 0.9; O₂-in could be used as a proxy for VCC. Oxygen mass transfer (kLa) was also quantified throughout the culture period with antifoam addition at different time points which is shown to lower the kLa. Real-time specific oxygen consumption rate (qO₂) of 2–20 pmol/cell/day throughout the bioreactor runs were within the range of values reported in literature for mammalian cell cultures. We also report, to our knowledge, the first instance of a distinct correlation between respiration quotient (RQ) and the metabolic state of the cell culture with regard to lactate production phase (average RQ > 1) and consumption phase (average RQ < 1).

Balancing Container Closure Integrity and Aesthetics for a Robust Aseptic or Sterile Vial Packaging System

Container closure integrity (CCI) is one of the requirements for a sterile packaging system. For vial-based systems, the capping process is a critical step in creating and ensuring an adequate seal with acceptable CCI. Container closure integrity tests (CCITs) such as the dye ingress and the helium leak rate are two methods among many that, in the appropriate scenario, help to challenge this required attribute. The use of locked-in stopper compression (compression under the crimp seal post capping) enables correlation of these methods to CCI and seal quality. In fact, the overall acceptability of a seal can be evaluated using quantitative and qualitative methods. Usually lost in these assessments is the existence of seal cosmetics as an essential additional seal quality attribute. Unacceptable cosmetic quality can have a major impact on manufacturing (reduced batch output, high yield cost, etc.) and user (perceived low quality, brand image, potential injury, etc.) experiences. Interestingly, the aesthetics of a seal is also impacted by the capping process which is quite complicated because the acceptance criteria for aesthetics of a seal is subjective. Ultimately, this affects commercial manufacturing efficiency and CCI. Here, we present a simple methodology for package selection and evaluated multiple package configurations using locked-in stopper compression (through residual seal force, RSF) measurements and seal aesthetics analyses (using a semi-quantitative aesthetics scale). The integrity of the seals was analyzed using multiple CCIT methods. We determined that component dimensions such as the seal length play a major role in obtaining proper seal aesthetics and integrity. This can ultimately enable the selection of robust packaging components that provide an adequate range of manufacturing conditions without cosmetic defects. A failure to do this could result in high rejects during drug product visual inspection culminating in low batch yield, high costs or could pose harm to patients if suitable CCI is not achieved.LAY ABSTRACT: One common container closure system for parenteral drug products includes a glass vial, rubber stopper, and aluminum crimp seal. The capping process, in which the elastomeric closure is compressed against the vial by means of an aluminum crimp seal, is key to ensuring an optimal seal from both an aesthetic and CCI perspective. Ensuring a robust capping process must include a deep and necessary understanding of the interconnection between the selected components, desired aesthetics of the seal, stopper compression, residual seal force, and CCI; the way in which the capper is configured (sealing parameters) will play a part in addition to the "style" used in manufacturing. Previous published studies have focused on capping process controls to only ensure CCI. Here, we present a useful methodology for selecting appropriate components and capping process parameters using a scaled-down approach to achieve elegant seal quality and CCI simultaneously. Dimensional analysis and capping design of experiments (DOEs) were conducted on lab-scale equipment that was representative of commercial configurations. The seals made from these studies were analyzed using residual seal force, helium leak, and dye ingress methods. The results and their implications were discussed with regard to the operating principle of the rail-type capping machine.

A perspective on Quality-by-Control (QbC) in pharmaceutical continuous manufacturing

The Quality-by-Design (QbD) guidance issued by the US Food and Drug Administration (FDA) has catalyzed the modernization of pharmaceutical manufacturing practices including the adoption of continuous manufacturing. Active process control was highlighted recently as a means to improve the QbD implementation. This advance has since been evolving into the concept of Quality-by-Control (QbC). In this study, the concept of QbC is discussed, including a definition of QbC, a review of the recent developments towards the QbC, and a perspective on the challenges of QbC implementation in continuous manufacturing. The QbC concept is demonstrated using a rotary tablet press, integrated into a pilot scale continuous direct compaction process. The results conclusively showed that active process control, based on product and process knowledge and advanced model-based techniques, including data reconciliation, model predictive control (MPC), and risk analysis, is indispensable to comprehensive QbC implementation, and ensures robustness and efficiency.

Design and experimental realization of a robust decentralized PI controller for a coupled tank system

This paper presents design and realization of a robust decentralized PI controller for regulating the level of a coupled tank system. The proposed controller is designed based on a predefined reference transfer function model in which we adopt a frequency matching of actual and reference models. Realization of control algorithms for a multivariable system is often complicated owing to uncertainties in the process dynamics. In this paper, initially a frequency response fitting model reduction technique is adopted to obtain a First Order Plus Dead Time (FOPDT) model of each higher order decoupled subsystem. Further, using the obtained reduced order model, the proposed robust decentralized PI controller is designed. The stability and performance of the proposed controller are verified by considering multiplicative input and output uncertainties. The performance of the proposed robust decentralized controller has been compared with that of a decentralized PI controller. To validate the performance of the proposed control approach, real-time experimentation is pursed on a Feedback Instrument manufactured coupled tank system.

Real-time monitoring of particle size distribution in a continuous granulation and drying process by near infrared spectroscopy

In continuous granulation, it can be important to control granules particle size distribution (PSD), as it may affect final product quality. Near infrared spectroscopy (NIRS) is already a routine analytical procedure within pharmaceutical continuous manufacturing for the in-line analysis of chemical material-characteristics. Consequently, the extraction of additional information related to granules' physical properties like particle size distribution is tempting, as it would enhance process knowledge without the need for new capital investments. Three in-line NIRS methods were developed via partial least squares regression, to predict dried granules PSD-fractions X10, X50, and X90 within a GMP-qualified continuous twin-screw wet granulation and fluid-bed drying process. Methods were developed for the size range of 20-234 µm (X10), 98-1017 µm (X50), and 748-2297 µm (X90) and assessed with one internal and three external validation datasets in agreement with current guidelines on NIRS. Internal validation indicated root mean square error of predictions (RMSEPs) of 17 µm, 97 µm, and 174 µm, for PSD X10, X50, and X90 respectively, with acceptable linearity, slope, and bias. Furthermore, the ratio of prediction to deviation (RPD), the ratio of prediction error to laboratory error (PRL), and the range error ratio (RER) were evaluated, with all values within the acceptance range for adequate to good NIR methods (1.75 > RPD < 3, PRL ≤ 2, RER ≥ 10). Methods applicability to in-line processes and their robustness towards water content and active pharmaceutical ingredient content was further demonstrated with three independent in-line datasets in real-time, showing good agreement between predicted and reference values. In summary, methods demonstrated to be sufficient for their intended purpose to monitor trends and sudden changes in dried granules PSD during continuous granulation and drying. Because of their fast response time, they are unique tools to characterize the dynamic behavior and navigate the agglomeration state of the material in static and transient process conditions during continuous granulation and drying.

The generalized forced oscillation method for tuning PID controllers

In this paper a generalized tuning methodology for proportional-integral-derivative (PID) controllers is proposed. The methodology is akin to the Ziegler-Nichols forced oscillation method, inheriting fully its practical appeal, but can be applied to much more general classes of plants. This generalization is achieved by employing a relay with adjustable phase (RAP) in a relay feedback experiment, and the tuning consists of formulas based on measurements obtained from this experiment. Experimental results in a liquid processing plant with of-the-shelf industrial equipments illustrate the performance of the methodology and the practical procedures involved in its application, which can be fully automated.

Optimized robust control for industrial unstable process via the mirror-mapping method

In this work, a novel optimized robust control algorithm, based on the mirror-mapping method, is proposed for a class of industrial unstable process with time delay. The optimizing criterion is to minimize the sensitivity function to enhance its robustness. The controllers are designed based on the Padé approximated mirror-mapping process with a stable form, other than the original unstable system. The developed algorithm could release the internal stability constraints to the unstable plant. By using the graphical stability criterion, a systematic methodology is derived to obtain the exact stabilizing region, where the sole design parameter is related to the stability degree of the closed-loop system. The proposed algorithm is with characteristics of concise and efficient design. Three experiments has been employed to illustrate that the control effects can achieve the satisfied performance in aspects of disturbance rejection and robustness.

Residual Seal Force Testing: A Suitable Method for Seal Quality Determination of (High Potent) Parenterals

Vial capping plays a critical role in the drug product manufacturing process owing to the complex interplay of several adjustable process steps. Seal quality and integrity and containment assurance are essential for parenteral pharmaceuticals, as the vial's content may be contaminated or, in the case of highly potent drugs (e.g., antibody drug conjugates), may bear a risk of contamination. The residual seal force (RSF) method can enable further insight in capping equipment settings independently of the container closure system (CCS) and their resulting seal quality.The present study investigates the accuracy of the RSF method focusing on different force settings, RSF development over time, distance between capping plates and vial neck (roller-axis), time point of flip-off button removal, and internal and external vial pressure differences (flight simulation and vials closed under vacuum).Results show that the forces used on an RSF tester should be kept low to minimize CCS deformation, and a period of stable RSF values after the initial decrease should be implemented between capping and RSF measurement to increase accuracy. Variations in the distance between the capping plates and vial neck (roller-axis) can result in incomplete crimps or visual defects of the seals. In addition, the time point of flip-off button removal as part of the sample preparation had no significant impact on RSF measurements. Finally, pressure differences between the vial interior and exterior had no significant impact on the RSF data.LAY ABSTRACT: Vial capping plays a critical role in the drug product manufacturing process due to the complex interplay of several adjustable process steps. Seal quality, integrity, and containment are essential for parenteral pharmaceuticals, as the vial's content varies and may be contaminated, sensitive to stress, and/or highly potent (eg, antibody drug conjugates). The residual seal force (RSF) method can enable further insight in capping equipment settings independently of the container closure system and their resulting seal quality.In this study, we determined RSF values by applying different force settings of the RSF tester and investigated the influence of sample preparation on the determination of RSF. Furthermore, the capping process parameter roller-axis was evaluated by RSF and visual inspection. In addition, we investigated the influence of pressure differences of vials on the RSF as they occurred during air transport and products closed under vacuum.

Water reuse: An alternative to minimize the environmental impact on the leather industry

Tanneries are industries that may cause high environmental impact. Consequently, they are responsible for generating a large amount of wastewater with high concentrations of contaminants that require significant investment and operating costs in their treatment in order to accomplish the emission standards required by environmental legislation. This work has as main objective to minimize the environmental impact of the water used by tanneries through the study of reuse possibilities of wastewater tanning floats. The major concern is related to the pollutant chromium in the environment, since it is the tanning product that gives the best characteristics to leather and consequently the most used product. The reuse tests were performed on pilot and industrial scales. During the experiments, the following process control parameters were evaluated: pH, density, acid-base indicators for hide and shrinkage temperature. To validate the water reuse in tanneries, samples of wet-blue leather and residual floats were collected from pilot and industrial processes to perform chemical analyses to prove the validity of the process with reuse. Research has shown that reuse techniques, when properly evaluated, can be used in industries. In addition to reducing water demand, the reuse of wastewater in tanning processes minimizes the disposal of the wastewater with chromium and uses the residual chromium float.

A Quality-by-Control Approach in Pharmaceutical Continuous Manufacturing of Oral Solid Dosage via Direct Compaction

The pharmaceutical industry has been undergoing a paradigm shift towards continuous manufacturing, under which novel approaches to real-time product quality assurance have been investigated. A new perspective, entitled Quality-by-Control (QbC), has recently been proposed as an important extension and complementary approach to enable comprehensive Quality-by-Design (QbD) implementation. In this study, a QbC approach was demonstrated for a commercial scale tablet press in a continuous direct compaction process. First, the necessary understanding of the compressibility of a model formulation was obtained under QbD guidance using a pilot scale tablet press, Natoli BLP-16. Second, a data reconciliation strategy was used to reconcile the tablet weight measurement based on this understanding on a commercial scale tablet press, Natoli NP-400. Parameter estimation to monitor and update the material property variance was also considered. Third, a hierarchical three-level control strategy, which addressed the fast process dynamics of the commercial scale tablet press was designed. The strategy consisted of the Level 0 built-in machine control, Level 1 decoupled Proportional Integral Derivative (PID) control loops for tablet weight, pre-compression force, main compression force, and production rate control, and Level 2 data reconciliation of sensor measurements. The effective and reliable performance, which could be demonstrated on the rotary tablet press, confirmed that a QbC approach, based on product and process knowledge and advanced model-based techniques, can ensure robustness and efficiency in pharmaceutical continuous manufacturing.

Human performance measures for the evaluation of process control human-system interfaces in high-fidelity simulations

We reviewed the available literature on measuring human performance to evaluate human-system interfaces (HSIs), focused on high-fidelity simulations of industrial process control systems, to identify best practices and future directions for research and operations. We searched the available literature and then conducted in-depth review, structured coding, and analysis of 49 articles, which described 42 studies. Human performance measures were classified across six dimensions: task performance, workload, situation awareness, teamwork/collaboration, plant performance, and other cognitive performance indicators. Many studies measured performance in more than one dimension, but few studies addressed more than three dimensions. Only a few measures demonstrated acceptable levels of reliability, validity, and sensitivity in the reviewed studies in this research domain. More research is required to assess the measurement qualities of the commonly used measures. The results can provide guidance to direct future research and practice for human performance measurement in process control HSI design and deployment.

Real-time optimization of the key filtration parameters in an AnMBR: Urban wastewater mono-digestion vs. co-digestion with domestic food waste

This study describes a model-based method for real-time optimization of the key filtration parameters in a submerged anaerobic membrane bioreactor (AnMBR) treating urban wastewater (UWW) and UWW mixed with domestic food waste (FW). The method consists of an initial screening to find out adequate filtration conditions and a real-time optimizer applied to a periodically calibrated filtration model for minimizing the operating costs. The initial screening consists of two statistical analyses: (1) Morris screening method to identify the key filtration parameters; (2) Monte Carlo method to establish suitable initial control inputs values. The operating filtration cost after implementing the control methodology was €0.047 per m³ (59.6% corresponding to energy costs) when treating UWW and €0.067 per m³ when adding FW due to higher fouling rates. However, FW increased the biogas productivities, reducing the total costs to €0.035 per m³. Average downtimes for reversible fouling removal of 0.4% and 1.6% were obtained, respectively. The results confirm the capability of the proposed control system for optimizing the AnMBR performance when treating both substrates.

Data-based approach for feedback-feedforward controller design using closed-loop plant data

Feedforward control of measurable disturbances is a useful complement to feedback control because feedforward control performs control actions before a disturbance response occurs in a process output. In contrast to the typical model-based design approach, a novel data-based method for designing both the feedback and feedforward controllers is presented in this paper. The controller design directly exploits closed-loop plant data and does not require an identification of process and disturbance models. A feedback proportional-integral-derivative controller and feedforward lead-lag compensator are sequentially designed on the basis of the closed-loop response data for a set-point change and for a disturbance input, respectively. Because the controller design process uses plant data integrals, the proposed method is robust against measurement noise. Moreover, the proposed design method can be applied to improve existing underperforming feedback and feedforward controllers using routine closed-loop operating data. Simulation studies demonstrated that the proposed method outperforms existing methods in designing a feedback-feedforward control system.