Artificial intelligence modeling to predict transmembrane pressure in anaerobic membrane bioreactor-sequencing batch reactor during biohydrogen production

Machine learning algorithms for predicting membrane bioreactors performance: A review

Membrane bioreactors (MBR) are recognized as a sustainable technology for treating polluted effluents. Machine learning (ML) algorithms have emerged as a modeling option to predict pollutant removal and operational variables such as membrane fouling, permeability, and energy consumption, which are significant challenges for MBR application. This review examines the use of ML algorithms in MBR-based wastewater treatment, focusing on the prediction of nitrogen and organic matter removal, and operational parameters related to membrane fouling. It presents the structures and fit quality of each model, noting that artificial neural networks (ANNs) are the most commonly used algorithm, appearing in 88 % of the 57 analyzed articles. Additionally, the review identified studies using random forests, support vector machines, k-nearest neighbors and boosting techniques, among other ML algorithms, although these were less frequently encountered. The review suggests potential in exploring less-utilized models for MBR data and identifies a gap in predicting membrane lifespan and replacement with ML models. This study aims to guide the development of new models for optimizing MBR performance by highlighting effective variables and algorithms, enhancing process control, real-time data analysis, parameter adjustment, and operational efficiency.

Advancements in biohydrogen production - a comprehensive review of technologies, lifecycle analysis, and future scope

The global shift towards sustainable energy sources, necessitated by climate change concerns, has led to a critical review of biohydrogen production (BHP) processes and their potential as a solution to environmental challenges. This review evaluates the efficiency of various reactors used in BHP, focusing on operational parameters such as substrate type, pH, temperature, hydraulic retention time (HRT), and organic loading rate (OLR). The highest yield reported in batch, continuous, and membrane reactors was in the range of 29-40 L H2/L per day at an OLR of 22-120 g/L per day, HRT of 2-3 h and acidic range of 4-6, with the temperature maintained at 37 °C. The highest yield achieved was 208.3 L H2/L per day when sugar beet molasses was used as a substrate with Clostridium at an OLR of 850 g COD/L per day, pH of 4.4, and at 8 h HRT. The integration of artificial intelligence (AI) tools, such as artificial neural networks and support vector machines has emerged as a novel approach for optimizing reactor performance and predicting outcomes. These AI models help in identifying key operational parameters and their optimal ranges, thus enhancing the efficiency and reliability of BHP processes. The review also draws attention to the importance of life cycle and techno-economic analyses in assessing the environmental impact and economic viability of BHP, addressing potential challenges like high operating costs and energy demands during scale-up. Future research should focus on developing more efficient and cost-effective BHP systems, integrating advanced AI techniques for real-time optimization, and conducting comprehensive LCA and TEA to ensure sustainable and economically viable biohydrogen production. By addressing these areas, BHP can become a key component of the transition to sustainable energy sources, contributing to the reduction of greenhouse gas emissions and the mitigation of environmental impacts associated with fossil fuel use.

Application of artificial intelligence tools in wastewater and waste gas treatment systems: Recent advances and prospects

The non-linear complex relationships among the process variables in wastewater and waste gas treatment systems possess a significant challenge for real-time systems modelling. Data driven artificial intelligence (AI) tools are increasingly being adopted to predict the process performance, cost-effective process monitoring, and the control of different waste treatment systems, including those involving resource recovery. This review presents an in-depth analysis of the applications of emerging AI tools in physico-chemical and biological processes for the treatment of air pollutants, water and wastewater, and resource recovery processes. Additionally, the successful implementation of AI-controlled wastewater and waste gas treatment systems, along with real-time monitoring at the industrial scale are discussed.

Enhancing sequencing batch reactors for efficient wastewater treatment across diverse applications: A comprehensive review

This review explores recent progress in sequencing batch reactors (SBRs) and hybrid systems for wastewater treatment, emphasizing their adaptability and effectiveness in managing diverse wastewater compositions. Through extensive literature analysis from 1985 to 2024, the integration of advanced technologies like photocatalysis within hybrid systems is highlighted, showing promise for improved pollutant removal efficiencies. Insights into operational parameters, reactor design, and microbial communities influencing SBR performance are discussed. Sequencing batch biofilm reactors (SBBRs) demonstrate exceptional efficiency in Chemical Oxygen Demand, nitrogen, and phosphorus removal, while innovative anaerobic-aerobic-anoxic sequencing batch reactors (AOA-SBRs) offer effective nutrient removal strategies. Hybrid systems, particularly photocatalytic sequencing batch reactors (PSBRs), show potential for removing persistent pollutants like antibiotics and phenols, underscoring the significance of advanced oxidation processes. However, research gaps persist, including the need for comparative studies between different SBR types and comprehensive evaluations of long-term performance, environmental variability, and economic viability. Addressing these gaps will be vital for the practical deployment of SBRs and hybrid systems. Further exploration of synergies, economic considerations, and reactor stability will enhance the sustainability and scalability of these technologies for efficient and eco-friendly wastewater treatment.

Potential and challenge in accelerating high-value conversion of CO2 in microbial electrosynthesis system via data-driven approach

Microbial electrosynthesis for CO2 utilization (MESCU) producing valuable chemicals with high energy density has garnered attention due to its long-term stability and high coulombic efficiency. The data-driven approaches offer a promising avenue by leveraging existing data to uncover the underlying patterns. This comprehensive review firstly uncovered the potentials of utilizing data-driven approaches to enhance high-value conversion of CO2 via MESCU. Firstly, critical challenges of MESCU advancing have been identified, including reactor configuration, cathode design, and microbial analysis. Subsequently, the potential of data-driven approaches to tackle the corresponding challenges, encompassing the identification of pivotal parameters governing reactor setup and cathode design, alongside the decipheration of omics data derived from microbial communities, have been discussed. Correspondingly, the future direction of data-driven approaches in assisting the application of MESCU has been addressed. This review offers guidance and theoretical support for future data-driven applications to accelerate MESCU research and potential industrialization.

Integrating artificial intelligence modeling and membrane technologies for advanced wastewater treatment: Research progress and future perspectives

Membrane technologies have become proficient alternatives for advanced wastewater treatment, ensuring high contaminant removal and sustainable resource recovery. Despite significant progress, ongoing research efforts aim to further optimize treatment performance. Among the challenges faced, membrane fouling persists as a relevant obstacle in membrane technologies, necessitating the development of more effective mitigation strategies. Mathematical models, widely employed for predicting treatment performance, generally exhibit low accuracy and suffer from uncertainties due to the complex and variable nature of wastewater. To overcome these limitations, numerous studies have proposed artificial intelligence (AI) modeling to accurately predict membrane technologies' performance and fouling mechanisms. This approach aims to provide advanced simulations and predictions, thereby enhancing process control, optimization, and intensification. This literature review explores recent advancements in modeling membrane-based wastewater treatment processes through AI models. The analysis highlights the enormous potential of this research field in enhancing the efficiency of membrane technologies. The role of AI modeling in defining optimal operating conditions, developing effective strategies for membrane fouling mitigation, enhancing the performance of novel membrane-based technologies, and improving membrane fabrication techniques is discussed. These enhanced process optimization and control strategies driven by AI modeling ensure improved effluent quality, optimized resource consumption, and minimized operating costs. The potential contribution of this cutting-edge approach to a paradigm shift toward sustainable wastewater treatment is examined. Finally, this review outlines future perspectives, emphasizing the research challenges that require attention to overcome the current limitations hindering the integration of AI modeling in wastewater treatment plants.

Predictive modeling based on artificial neural networks for membrane fouling in a large pilot-scale anaerobic membrane bioreactor for treating real municipal wastewater

Membrane fouling is the primary obstacle to applying anaerobic membrane bioreactors (AnMBRs) in municipal wastewater treatment. This issue holds critical significance as efficient wastewater treatment serves as a cornerstone for achieving environmental sustainability. This study uses machine learning to predict membrane fouling, taking advantage of rapid computational and algorithmic advances. Based on the 525-day operation data of a large pilot-scale AnMBR for treating real municipal wastewater, regression prediction was realized using multilayer perceptron (MLP) and long short-term memory (LSTM) artificial neural networks under substantial variations in operating conditions. The models involved employing the organic loading rate, suspended solids concentration, protein concentration in extracellular polymeric substance (EPSp), polysaccharide concentration in EPS (EPSc), reactor temperature, and flux as input features, and transmembrane pressure as the prediction target output. Hyperparameter optimization enhanced the regression prediction accuracies, and the rationality and utility of the MLP model for predicting large-scale AnMBR membrane fouling were confirmed at global and local levels of interpretability analysis. This work not only provides a methodological advance but also underscores the importance of merging environmental engineering with computational advancements to address pressing environmental challenges.

WITHDRAWN: Effects of hydraulic retention time and cultivation on nutrients removal and biomass production in wastewater by membrane photobioreactor: Modeling and optimization by machine learning and response surface methodology

This article has been withdrawn at the request of the Editor-in-Chief.

Energy balance survey for the design and auto-thermal thermophilic aerobic digestion of algal-based membrane bioreactor for Landfill Leachate Treatment(under organic loading rates): Experimental and simulation-based ANN and NSGA-II

Although algal-based membrane bioreactors (AMBRs) have been demonstrated to be effective in treating wastewater (landfill leachate), there needs to be more research into the effectiveness of these systems. This study aims to determine whether AMBR is effective in treating landfill leachate with hydraulic retention times (HRTs) of 8, 12, 14, 16, 21, and 24 h to maximize AMBR's energy efficiency, microalgal biomass production, and removal efficiency using artificial neural network (ANN) models. Experimental results and simulations indicate that biomass production in bioreactors depends heavily on HRT. A decrease in HRT increases algal (Chlorella vulgaris) biomass productivity. Results also showed that 80% of chemical oxygen demand (COD) was removed from algal biomass by bioreactors. To determine the most efficient way to process the features as mentioned above, nondominated sorting genetic algorithm II (NSGA-II) techniques were applied. A mesophilic, suspended-thermophilic, and attached-thermophilic organic loading rate (OLR) of 1.28, 1.06, and 2 kg/m3/day was obtained for each method. Compared to suspended-thermophilic growth (3.43 kg/m3.day) and mesophilic growth (1.28 kg/m3.day), attached-thermophilic growth has a critical loading rate of 10.5 kg/m3.day. An energy audit and an assessment of the system's auto-thermality were performed at the end of the calculation using the Monod equation for biomass production rate (Y) and bacteria death constant (Kd). According to the results, a high removal level of COD (at least 4000 mg COD/liter) leads to auto-thermality.

Neuro-fuzzy modelling of a continuous stirred tank bioreactor with ceramic membrane technology for treating petroleum refinery effluent: a case study from Assam, India

A continuous stirred tank bioreactor (CSTB) with cell recycling combined with ceramic membrane technology and inoculated with Rhodococcus opacus PD630 was employed to treat petroleum refinery wastewater for simultaneous chemical oxygen demand (COD) removal and lipid production from the retentate obtained during wastewater treatment. In the present study, the COD removal efficiency (CODRE) (%) and lipid concentration (g/L) were predicted using two artificial intelligence models, i.e., an artificial neural network (ANN) and a neuro-fuzzy neural network (NF-NN) with a network topology of 6-25-2 being the best for NF-NN. The results revealed the superiority of NF-NN over ANN in terms of determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE). Three learning algorithms were tested with NF-NN; among them, the Bayesian regularization backpropagation (BR-BP) outperformed others. The sensitivity analysis revealed that, if solid retention time and biomass concentrations were maintained between 35 and 75 h and 3.0 g/L and 3.5 g/L, respectively, high CODRE (93%) and lipid concentration (2.8 g/L) could be obtained consistently.

Towards industrial biological hydrogen production: a review

Increased production of renewable energy sources is becoming increasingly needed. Amidst other strategies, one promising technology that could help achieve this goal is biological hydrogen production. This technology uses micro-organisms to convert organic matter into hydrogen gas, a clean and versatile fuel that can be used in a wide range of applications. While biohydrogen production is in its early stages, several challenges must be addressed for biological hydrogen production to become a viable commercial solution. From an experimental perspective, the need to improve the efficiency of hydrogen production, the optimization strategy of the microbial consortia, and the reduction in costs associated with the process is still required. From a scale-up perspective, novel strategies (such as modelling and experimental validation) need to be discussed to facilitate this hydrogen production process. Hence, this review considers hydrogen production, not within the framework of a particular production method or technique, but rather outlines the work (bioreactor modes and configurations, modelling, and techno-economic and life cycle assessment) that has been done in the field as a whole. This type of analysis allows for the abstraction of the biohydrogen production technology industrially, giving insights into novel applications, cross-pollination of separate lines of inquiry, and giving a reference point for researchers and industrial developers in the field of biohydrogen production.

A Mechanistic Model for Hydrogen Production in an AnMBR Treating High Strength Wastewater

In the global race to produce green hydrogen, wastewater-to-H2 is a sustainable alternative that remains unexploited. Efficient technologies for wastewater-to-H2 are still in their developmental stages, and urgent process intensification is required. In our study, a mechanistic model was developed to characterize hydrogen production in an AnMBR treating high-strength wastewater (COD > 1000 mg/L). Two aspects differentiate our model from existing literature: First, the model input is a multi-substrate wastewater that includes fractions of proteins, carbohydrates, and lipids. Second, the model integrates the ADM1 model with physical/biochemical processes that affect membrane performance (e.g., membrane fouling). The model includes mass balances of 27 variables in a transient state, where metabolites, extracellular polymeric substances, soluble microbial products, and surface membrane density were included. Model results showed the hydrogen production rate was higher when treating amino acids and sugar-rich influents, which is strongly related to higher EPS generation during the digestion of these metabolites. The highest H2 production rate for amino acid-rich influents was 6.1 LH2/L-d; for sugar-rich influents was 5.9 LH2/L-d; and for lipid-rich influents was 0.7 LH2/L-d. Modeled membrane fouling and backwashing cycles showed extreme behaviors for amino- and fatty-acid-rich substrates. Our model helps to identify operational constraints for H2 production in AnMBRs, providing a valuable tool for the design of fermentative/anaerobic MBR systems toward energy recovery.

A survey on advanced machine learning and deep learning techniques assisting in renewable energy generation

The sustainability of the earth depends on renewable energy. Forecasting the output of renewable energy has a big impact on how we operate and manage our power networks. Accurate forecasting of renewable energy generation is crucial to ensuring grid dependability and permanence and reducing the risk and cost of the energy market and infrastructure. Although there are several approaches to forecasting solar radiation on a global scale, the two most common ones are machine learning algorithms and cloud pictures combined with physical models. The objective is to present a summary of machine learning-based techniques for solar irradiation forecasting in this context. Renewable energy is being used more and more in the world's energy grid. Numerous strategies, including hybrids, physical models, statistical approaches, and artificial intelligence techniques, have been developed to anticipate the use of renewable energy. This paper examines methods for forecasting renewable energy based on deep learning and machine learning. Review and analysis of deep learning and machine learning forecasts for renewable energy come first. The second paragraph describes metaheuristic optimization techniques for renewable energy. The third topic was the open issue of projecting renewable energy. I will wrap up with a few potential future job objectives.

Artificial intelligence in wastewater treatment: A data-driven analysis of status and trends

Wastewater treatment is a complex process that involves many uncertainties, leading to fluctuations in effluent quality and costs, and environmental risks. Artificial intelligence (AI) can handle complex nonlinear problems and has become a powerful tool for exploring and managing wastewater treatment systems. This study provides a summary of the current status and trends in AI research as applied to wastewater treatment, based on published papers and patents. Our results indicate that, at present, AI is primarily used to evaluate removal of pollutants (conventional, typical, and emerging contaminants), optimize models and process parameters, and control membrane fouling. Future research will likely continue to focus on removal of phosphorus, organic pollutants, and emerging contaminants. Moreover, analyzing microbial community dynamics and achieving multi-objective optimization are promising directions of research. The knowledge map shows that there may be future technological innovation related to predicting water quality under specific conditions, integrating AI with other information technologies and utilizing image-based AI and other algorithms in wastewater treatment. In addition, we briefly review development of artificial neural networks (ANNs) and explore the evolutionary path of AI in wastewater treatment. Our findings provide valuable insights into potential opportunities and challenges for researchers applying AI to wastewater treatment.

A novel IEF-DLNN and multi-objective based optimizing control strategy for seawater reverse osmosis desalination plant

Over the past years, Seawater Desalination (SWD) has been enhanced regularly. In this desalination process, numerous technologies are available. The Reverse Osmosis (RO) process, which requires effectual control strategies, is the most commercially-dominant technology. Therefore, for SWD, a novel Interpolation and Exponential Function-centered Deep Learning Neural Network (IEF-DLNN) and multi-objective-based optimizing control system has been proposed in this research methodology. Initially, the input data are gathered; then, to control the desalination process, an optimal control technique has been utilized by employing Probability-centric Dove Swarm Optimization-Proportional Integral Derivative (PDSO-PID). The attributes of permeate are extracted before entering the RO process; after that, by utilizing the IEF-DLNN, the trajectory is predicted. For optimal selection, the extracted attributes are deemed if the trajectory is present, or else to mitigate energy consumption along with cost, the RO Desalination (ROD) is performed. In an experimental evaluation, regarding certain performance metrics, the proposed model's performance is analogized with the prevailing methodologies. The outcomes demonstrated that the proposed system achieved better performance.

Machine learning in fermentative biohydrogen production: Advantages, challenges, and applications

Hydrogen can be produced in an environmentally friendly manner through biological processes using a variety of organic waste and biomass as feedstock. However, the complexity of biological processes limits their predictability and reliability, which hinders the scale-up and dissemination. This article reviews contemporary research and perspectives on the application of machine learning in biohydrogen production technology. Several machine learning algorithems have recently been implemented for modeling the nonlinear and complex relationships among operational and performance parameters in biohydrogen production as well as predicting the process performance and microbial population dynamics. Reinforced machine learning methods exhibited precise state prediction and retrieved the underlying kinetics effectively. Machine-learning based prediction was also improved by using microbial sequencing data as input parameters. Further research on machine learning could be instrumental in designing a process control tool to maintain reliable hydrogen production performance and identify connection between the process performance and the microbial population.

A back propagation neural network model for accurately predicting the removal efficiency of ammonia nitrogen in wastewater treatment plants using different biological processes

Accurately predicting the water quality of treated water from a water treatment plant (WWTP) based on the obtained operating database is of great significance. However, it is difficult for common mechanistic models to work well. In this study, a back propagation artificial neural network (BPANN) model with high accuracy was developed to predict the denitrification efficiency based on a 1-year operating database. Standardized principal component analysis (PCA) methods were used to address the data, and the PCA processed data exhibited the best accuracy. In three WWTPs adopting the anaerobic/anoxic/oxic (A²O) process, the ammonia nitrogen removal efficiency of WWTPs was successfully predicted by using five variables: inlet flow rate, pH value, original ammonia nitrogen concentration, Chemical oxygen demand (COD) concentration, and total phosphorus concentration. Importantly, the obtained BPANN model can be effectively used for other widely used treatment processes, such as oxidation ditch (OD), sequencing batch reactor activated sludge process (SBR), membrane bioreactor (MBR), and cyclic activated sludge technology (CAST), by simply optimizing the training data ratios between 50/50 and 90/10. This is the first trial to set up a universal model for predicting the denitrification efficiency of WWTPs adopting common biological processes. The model could be used to choose the optimum treatment process in the new WWTP design or take action in advance to avoid the risk of excessive emissions when the already built WWTPs are subjected to sudden shocks.

Direct red 89 dye degradation by advanced oxidation process using sulfite and zero valent under ultraviolet irradiation: Toxicity assessment and adaptive neuro-fuzzy inference systems modeling

Sulfate-based advanced oxidation process mediated by zero-valent iron (ZVI) and ultraviolet radiation for the decomposition of sulfite salts resulted in the formation of strong oxidizing species (sulfate and hydroxide radicals) in aqueous solution is reported. Degradation of direct red 89 (DR89) dye via UV/ZVI/sulfite process was systematically investigated to evaluate the effect of pH, ZVI dose, sulfite, initial DR89 concentration, and reaction time on DR89 degradation. The synergy factor of UV/ZVI/sulfite process was found to be 2.23-times higher than the individual processes including ZVI, sulfite and UV. By increasing the ZVI dose from 100 mg/L to 300 mg/L, dye degradation was linearly enhanced from 67.12 ± 3.36% to 82.40 ± 4.12% by the UV/ZVI/sulfite process due to enhanced ZVI corrosion and sulfite activation. The highest degradation efficiency of 99.61 ± 0.02% was observed at pH of 5.0, [ZVI]0 = 300 mg/L, and [sulfite]0 = 400 mg/L. Toxicity assessment by Lepidium sativum demonstrated that treated dye solution by UV/ZVI/sulfite was within the non-toxic range. The application of optimal adaptive neuro-fuzzy inference system (ANFIS) to predict DR89 degradation indicated high accuracy of ANFIS model (R2 = 0.97 and RMSE = 0.051) via the UV/ZVI/sulfite process. It is suggested that UV/ZVI/sulfite process is suitable for industrial wastewater treatment.

Predicting membrane fouling in a high solid AnMBR treating OFMSW leachate through a genetic algorithm and the optimization of a BP neural network model

Anaerobic membrane bioreactors are a promising technology in the treatment of high-strength wastewater; however, unpredictable membrane fouling largely limits their scale-up application. This study, therefore, adopted a backpropagation neural network model to predict the membrane filtration performance in a submerged system, which treats leachate from the organic fraction of municipal solid waste. Duration time, water yield flow, influent COD, pH, bulk sludge concentration, and the ratio of ΔTMP to filtration time were selected as input variables to simulate membrane permeability. The membrane pressure slightly increased by 1.1 kPa within 62 days of operation. The results showed that the AnMBR membrane filtration performance was acceptable when treating OFMSW leachate under a flux of 6 L/(m²·h). The model results indicated that the sludge concentration largely determined the membrane fouling with a contribution of 33.8%. Given the local minimization problem in the BP neural network process, a genetic algorithm was introduced to optimize the simulation process, and the relative error of the results was reduced from 5.57% to 3.57%. Conclusively, the artificial neural network could be a useful tool for the prediction of an AnMBR that is so far under development.

Performance evaluation of whey flux in dead-end and cross-flow modes via convolutional neural networks

Effluent originating from cheese production puts pressure onto environment due to its high organic load. Therefore, the main objective of this work was to compare the influence of different process variables (transmembrane pressure (TMP), Reynolds number and feed pH) on whey protein recovery from synthetic and industrial cheese whey using polyethersulfone (PES 30 kDa) membrane in dead-end and cross-flow modes. Analysis on the fouling mechanistic model indicates that cake layer formation is dominant as compared to other pore blocking phenomena evaluated. Among the input variables, pH of whey protein solution has the biggest influence towards membrane flux and protein rejection performances. At pH 4, electrostatic attraction experienced by whey protein molecules prompted a decline in flux. Cross-flow filtration system exhibited a whey rejection value of 0.97 with an average flux of 69.40 L/m²h and at an experimental condition of 250 kPa and 8 for TMP and pH, respectively. The dynamic behavior of whey effluent flux was modeled using machine learning (ML) tool convolutional neural networks (CNN) and recursive one-step prediction scheme was utilized. Linear and non-linear correlation indicated that CNN model (R² - 0.99) correlated well with the dynamic flux experimental data. PES 30 kDa membrane displayed a total protein rejection coefficient of 0.96 with 55% of water recovery for the industrial cheese whey effluent. Overall, these filtration studies revealed that this dynamic whey flux data studies using the CNN modeling also has a wider scope as it can be applied in sensor tuning to monitor flux online by means of enhancing whey recovery efficiency.

Production of biofuels from biomass: Predicting the energy employing artificial intelligence modelling

Bioenergy may be a major replacement of fossil fuels which can make the path easier for sustainable development and decrease the dependency on conventional sources of energy. The main concern with the bioenergy is the availability of feedstock, dealing with its economics as well as its demand and supply chain management. This review deals with the finding of distinct potential of different Artificial Intelligence technologies focusing the challenges in bioenergy production system and its overall improvement in application. The study also highlights the contribution of Artificial Intelligence techniques for the prediction of energy from biomass and evaluates the computing-reasoning techniques for managing bioenergy production, biomass supply chain and optimization of process parameters for efficient bioconversion technologies.