The Role of Big Data in Industrial (Bio)chemical Process Operations

COVERT: A classless approach to generating balanced datasets for process modelling

In this work, a classless oversampling technique, Covert, was developed to improve historical datasets from industrial processing plants to aid process modelling. Using kernel density estimation and nearest neighbour algorithms, sparse regions are identified and resampled, developing a more balanced dataset. When applied to a real dataset from a geothermal power plant, Covert outperforms current best practice (Smote) in uniformly populating the input feature space and generating credible data in the output variable. When used to develop a data-driven model Covert improved model accuracy by 20% when predicting outside the original data's feature space. Smote, however, reduced model accuracy by 6% in the same feature space. Developing reliable models of industrial processes continues to be a significant hurdle in developing a digital twin. Using Covert, existing imbalanced historical data can be used to extend the range of applicability of any process model.

Current challenges and recent advances on the path towards continuous biomanufacturing

Continuous biopharmaceutical manufacturing is currently a field of intense research due to its potential to make the entire production process more optimal for the modern, ever-evolving biopharmaceutical market. Compared to traditional batch manufacturing, continuous bioprocessing is more efficient, adjustable, and sustainable and has reduced capital costs. However, despite its clear advantages, continuous bioprocessing is yet to be widely adopted in commercial manufacturing. This article provides an overview of the technological roadblocks for extensive adoptions and points out the recent advances that could help overcome them. In total, three key areas for improvement are identified: Quality by Design (QbD) implementation, integration of upstream and downstream technologies, and data and knowledge management. First, the challenges to QbD implementation are explored. Specifically, process control, process analytical technology (PAT), critical process parameter (CPP) identification, and mathematical models for bioprocess control and design are recognized as crucial for successful QbD realizations. Next, the difficulties of end-to-end process integration are examined, with a particular emphasis on downstream processing. Finally, the problem of data and knowledge management and its potential solutions are outlined where ontologies and data standards are pointed out as key drivers of progress.

Development of two devices for high-throughput screening of ethanol-producing microorganisms by real-time CO2 production monitoring

Bioethanol's importance as a renewable energy carrier led to the development of new devices for the high-throughput screening (HTS) of ethanol-producing microorganisms, monitoring ethanol production, and process optimization. This study developed two devices based on measuring CO₂ evolution (an equimolar byproduct of microbial ethanol fermentation) to allow for a fast and robust HTS of ethanol-producing microorganisms for industrial purposes. First, a pH-based system for identifying ethanol producers (Ethanol-HTS) was established in a 96-well plate format where CO₂ emission is captured by a 3D-printed silicone lid and transferred from the fermentation well to a reagent containing bromothymol blue as a pH indicator. Second, a self-made CO₂ flow meter (CFM) was developed as a lab-scale tool for real-time quantification of ethanol production. This CFM contains four chambers to simultaneously apply different fermentation treatments while LCD and serial ports allow fast and easy data transfer. Applying ethanol-HTS with various yeast concentrations and yeast strains displayed different colors, from dark blue to dark and light green, based on the amount of carbonic acid formed. The results of the CFM device revealed a fermentation profile. The curve of CO₂ production flow among six replications showed the same pattern in all batches. The comparison of final ethanol concentrations calculated based on CO₂ flow by the CFM device with the GC analysis showed 3% difference which is not significant. Data validation of both devices demonstrated their applicability for screening novel bioethanol-producer strains, determining carbohydrate fermentation profiles, and monitoring ethanol production in real time.

Assessment of Situation Awareness Conflict Risk between Human and AI in Process System Operation

The conflict between human and artificial intelligence is a critical issue, which has recently been introduced in Process System Engineering, capturing the observation and action conflicts. Interpretation conflict is another source of potential conflict that can cause serious concern for process safety as it is often perceived as confusion, surprise, or a mistake. It is intangible and associated with situation awareness. However, interpretation conflict has not been studied with the required emphasis. The current work proposes a novel methodology to quantify interpretation conflict probability and risk. The methodology is demonstrated, tested, and validated on a two-phase separator. The results show that interpretation conflict is usually hidden, mixed, or covered by traditional faults, and noises in observation and interpretation, including sensor faults, logic errors, cyberattacks, human mistakes, and misunderstandings, may easily trigger interpretation conflict. The proposed methodology will serve as a mechanism to develop strategies to manage interpretation conflict.

Digital models in biotechnology: Towards multi-scale integration and implementation

Industrial biotechnology encompasses a large area of multi-scale and multi-disciplinary research activities. With the recent megatrend of digitalization sweeping across all industries, there is an increased focus in the biotechnology industry on developing, integrating and applying digital models to improve all aspects of industrial biotechnology. Given the rapid development of this field, we systematically classify the state-of-art modelling concepts applied at different scales in industrial biotechnology and critically discuss their current usage, advantages and limitations. Further, we critically analyzed current strategies to couple cell models with computational fluid dynamics to study the performance of industrial microorganisms in large-scale bioprocesses, which is of crucial importance for the bio-based production industries. One of the most challenging aspects in this context is gathering intracellular data under industrially relevant conditions. Towards comprehensive models, we discuss how different scale-down concepts combined with appropriate analytical tools can capture intracellular states of single cells. We finally illustrated how the efforts could be used to develop digitals models suitable for both cell factory design and process optimization at industrial scales in the future.

Industrial data science – a review of machine learning applications for chemical and process industries

Understand and optimize industrial processes via machine learning and chemical engineering principles.

Big data analytics opportunities for applications in process engineering

Big data is an expression for massive data sets consisting of both structured and unstructured data that are particularly difficult to store, analyze and visualize. Big data analytics has the potential to help companies or organizations improve operations as well as disclose hidden patterns and secret correlations to make faster and intelligent decisions. This article provides useful information on this emerging and promising field for companies, industries, and researchers to gain a richer and deeper insight into advancements. Initially, an overview of big data content, key characteristics, and related topics are presented. The paper also highlights a systematic review of available big data techniques and analytics. The available big data analytics tools and platforms are categorized. Besides, this article discusses recent applications of big data in chemical industries to increase understanding and encourage its implementation in their engineering processes as much as possible. Finally, by emphasizing the adoption of big data analytics in various areas of process engineering, the aim is to provide a practical vision of big data.

Towards Digitalization in Bio-Manufacturing Operations: A Survey on Application of Big Data and Digital Twin Concepts in Denmark

Digitalization in the form of Big Data and Digital Twin inspired applications are hot topics in today's bio-manufacturing organizations. As a result, many organizations are diverting resources (personnel and equipment) to these applications. In this manuscript, a targeted survey was conducted amongst individuals from the Danish biotech industry to understand the current state and perceived future obstacles in implementing digitalization concepts in biotech production processes. The survey consisted of 13 questions related to the current level of application of 1) Big Data analytics and 2) Digital Twins, as well as obstacles to expanding these applications. Overall, 33 individuals responded to the survey, a group spanning from bio-chemical to biopharmaceutical production. Over 73% of the respondents indicated that their organization has an enterprise-wide level plan for digitalization, it can be concluded that the digitalization drive in the Danish biotech industry is well underway. However, only 30% of the respondents reported a well-established business case for the digitalization applications in their organization. This is a strong indication that the value proposition for digitalization applications is somewhat ambiguous. Further, it was reported that digital twin applications (58%) were more widely used than Big Data analytic tools (37%). On top of the lack of a business case, organizational readiness was identified as a critical hurdle that needs to be overcome for both Digital Twin and Big Data applications. Infrastructure was another key hurdle for implementation, with only 6% of the respondents stating that their production processes were 100% covered by advanced process analytical technologies.

Backstepping Methodology to Troubleshoot Plant-Wide Batch Processes in Data-Rich Industrial Environments

Troubleshooting batch processes at a plant-wide level requires first finding the unit causing the fault, and then understanding why the fault occurs in that unit. Whereas in the literature case studies discussing the latter issue abound, little attention has been given so far to the former, which is complex for several reasons: the processing units are often operated in a non-sequential way, with unusual series-parallel arrangements; holding vessels may be required to compensate for lack of production capacity, and reacting phenomena can occur in these vessels; and the evidence of batch abnormality may be available only from the end unit and at the end of the production cycle. We propose a structured methodology to assist the troubleshooting of plant-wide batch processes in data-rich environments where multivariate statistical techniques can be exploited. Namely, we first analyze the last unit wherein the fault manifests itself, and we then step back across the units through the process flow diagram (according to the manufacturing recipe) until the fault cannot be detected by the available field sensors any more. That enables us to isolate the unit wherefrom the fault originates. Interrogation of multivariate statistical models for that unit coupled to engineering judgement allow identifying the most likely root cause of the fault. We apply the proposed methodology to troubleshoot a complex industrial batch process that manufactures a specialty chemical, where productivity was originally limited by unexplained variability of the final product quality. Correction of the fault allowed for a significant increase in productivity.

Development of Robust and Physically Interpretable Soft Sensor for Industrial Distillation Column Using Transfer Learning with Small Datasets

In the development of soft sensors for industrial processes, the availability of data for data-driven modeling is usually limited, which led to overfitting and lack of interpretability when conventional deep learning models were used. In this study, the proposed soft sensor development methodology combining first-principle simulations and transfer learning was used to address these problems. Source-domain models were obtained using a large amount of data generated by dynamic simulations. They were then fine-tuned by a limited amount of real plant data to improve their prediction accuracies on the target domain and guaranteed the models with correct domain knowledge. An industrial C4 separation column operating at a refining unit was used as an example to illustrate the effectiveness of this approach. Results showed that fine-tuned networks could obtain better accuracy and improved interpretability compared to a simple feedforward network with or without regularization, especially when the amount of actual data available was small. For some secondary effects, such as interaction gain, its interpretability is mainly based on the interpretability of the corresponding source models.

Living and Prototyping Digital Twins for Urban Water Systems: Towards Multi-Purpose Value Creation Using Models and Sensors

In this paper, we review the emerging concept of digital twins (DTs) for urban water systems (UWS) based on the literature, stakeholder interviews and analyzing the current DT implementation process in the utility company VCS Denmark (VCS). Here, DTs for UWS are placed in the context of DTs at the component, unit process/operation or hydraulic structure, treatment plant, system, city, and societal levels. A UWS DT is characterized as a systematic virtual representation of the elements and dynamics of the physical system, organized in a star-structure with a set of features connected by data links that are based on standards for open data. This allows the overall functionality to be broken down into smaller, tangible units (features), enabling microservices that communicate via data links to emerge (the most central feature), facilitated by application programing interfaces (APIs). Coupled to the physical system, simulation models and advanced analytics are among the most important features. We propose distinguishing between living and prototyping DTs, where the term “living” refers to coupling observations from an ever-changing physical twin (which may change with, e.g., urban growth) with a simulation model, through a data link connecting the two. A living DT is thus a near real-time representation of an UWS and can be used for operational and control purposes. A prototyping DT represents a scenario for the system without direct coupling to real-time observations, which can be used for design or planning. By acknowledging that different DTs exist, it is possible to identify the value-creation from DTs achieved by different end-users inside and outside a utility organization. Analyzing the DT workflow in VCS shows that a DT must be multifunctional, updateable, and adjustable to support potential value creation across the utility company. This study helps clarify key DT terminology for UWS and identifies steps to create a DT by building upon digital ecosystems (DEs) and open standards for data.

Towards the Development of Digital Twins for the Bio-manufacturing Industry

The bio-manufacturing industry, along with other process industries, now has the opportunity to be engaged in the latest industrial revolution, also known as Industry 4.0. To successfully accomplish this, a physical-to-digital-to-physical information loop should be carefully developed. One way to achieve this is, for example, through the implementation of digital twins (DTs), which are virtual copies of the processes. Therefore, in this paper, the focus is on understanding the needs and challenges faced by the bio-manufacturing industry when dealing with this digitalized paradigm. To do so, two major building blocks of a DT, data and models, are highlighted and discussed. Hence, firstly, data and their characteristics and collection strategies are examined as well as new methods and tools for data processing. Secondly, modelling approaches and their potential of being used in DTs are reviewed. Finally, we share our vision with regard to the use of DTs in the bio-manufacturing industry aiming at bringing the DT a step closer to its full potential and realization.

Why Is Batch Processing Still Dominating the Biologics Landscape? Towards an Integrated Continuous Bioprocessing Alternative

Continuous manufacturing of biologics (biopharmaceuticals) has been an area of active research and development for many reasons, ranging from the demand for operational streamlining to the requirement of achieving obvious economic benefits. At the same time, biopharma strives to develop systems and concepts that can operate at similar scales for clinical and commercial production—using flexible infrastructures, such as single-use flow paths and small surge vessels. These developments should simplify technology transfer, reduce footprint and capital investment, and will allow to react readily to changing market pressures while maintaining quality attributes. Despite a number of clearly identified benefits compared to traditional batch processes, continuous bioprocessing is still not widely adopted for commercial manufacturing. This paper details how industry-specific technological, organizational, economic, and regulatory barriers that exist in biopharmaceutical manufacturing are hindering the adoption of continuous production processes. Based on this understanding, the roles of process systems engineering (PSE), process analytical technologies, and process modeling and simulation are highlighted as key enabling tools in overcoming these multi-faceted barriers in today’s manufacturing environment. Of course, we do recognize that there is also a need for a clear set of regulations to guide a transition of biologics manufacturing towards continuous processing. Furthermore, the role played by the emerging fields of process integration and automation as well as digitalization is explored, as these are the tools of the future to facilitate this transition from batch to continuous production. Finally, an outlook focusing on technology, management, and regulatory aspects is presented to identify key concerted efforts required to drive the broad adaptation of continuous manufacturing in biopharmaceutical processes.

Transforming data to information: A parallel hybrid model for real-time state estimation in lignocellulosic ethanol fermentation

Operating lignocellulosic fermentation processes to produce fuels and chemicals is challenging due to the inherent complexity and variability of the fermentation media. Real‐time monitoring is necessary to compensate for these challenges, but the traditional process monitoring methods fail to deliver actionable information that can be used to implement advanced control strategies. In this study, a hybrid‐modeling approach is presented to monitor cellulose‐to‐ethanol (EtOH) fermentations in real‐time. The hybrid approach uses a continuous‐discrete extended Kalman filter to reconciliate the predictions of a data‐driven model and a kinetic model and to estimate the concentration of glucose (Glu), xylose (Xyl), and EtOH. The data‐driven model is based on partial least squares (PLS) regression and predicts in real‐time the concentration of Glu, Xyl, and EtOH from spectra collected with attenuated total reflectance mid‐infrared spectroscopy. The estimations made by the hybrid approach, the data‐driven models and the internal model were compared in two validation experiments showing that the hybrid model significantly outperformed the PLS and improved the predictions of the internal model. Furthermore, the hybrid model delivered consistent estimates even when disturbances in the measurements occurred, demonstrating the robustness of the method. The consistency of the proposed hybrid model opens the doors towards the implementation of advanced feedback control schemes.

Towards smart biomanufacturing: a perspective on recent developments in industrial measurement and monitoring technologies for bio-based production processes

The biomanufacturing industry has now the opportunity to upgrade its production processes to be in harmony with the latest industrial revolution. Technology creates capabilities that enable smart manufacturing while still complying with unfolding regulations. However, many biomanufacturing companies, especially in the biopharma sector, still have a long way to go to fully benefit from smart manufacturing as they first need to transition their current operations to an information-driven future. One of the most significant obstacles towards the implementation of smart biomanufacturing is the collection of large sets of relevant data. Therefore, in this work, we both summarize the advances that have been made to date with regards to the monitoring and control of bioprocesses, and highlight some of the key technologies that have the potential to contribute to gathering big data. Empowering the current biomanufacturing industry to transition to Industry 4.0 operations allows for improved productivity through information-driven automation, not only by developing infrastructure, but also by introducing more advanced monitoring and control strategies.