Machine learning modeling and analysis of biohydrogen production from wastewater by dark fermentation process

Study on the Effect of the Electron Density-Characterized Groups on the Nitrogen Transformation during Coal Pyrolysis

This paper clarifies the effects of the functional groups on the nitrogen migration during coal pyrolysis by utilizing the density functional theory (DFT) calculations and the support vector regression (SVR) modeling. First, the study evidences the enhanced pyrolysis by electron-donating groups (EDGs) and inhibition by electron-withdrawing groups (EWGs). For example, for pyridine pyrolysis, the inclusion of -NH2 (EDG) is found to decrease the endothermicity and the maximal barrier involved in the HCN generation from 612.6 to 292.3 kJ/mol and from 624.2 to 296.0 kJ/mol, respectively. Second, DFT and Rdkit descriptors are filtered to constrain the SVR model to predict the activation energy and reaction energy. The results highlight the importance of the S_type descriptor. Finally, the TG-FTIR experiments using 2-pyridinecarboxylic acid and 2-hydroxypyridine as test samples are performed to validate the accelerated pyrolysis by the EDG group and the decelerated pyrolysis by EWG, showing accordance with our DFT calculations and SVR modeling. All of these findings will offer valuable insights for understanding the pyrolysis of coal.

Machine learning-enhanced modeling and characterization for optimizing dietary Fiber production from Highland barley bran

This study investigated the modification of highland barley bran through co-fermentation of Lactobacillus bulgaricus and Kluyveromyces marxianus, and developed a dynamic prediction model for DF content under these co-fermentation conditions using machine learning algorithms. The results showed that the XGBoost algorithm could predict changes in the DF component content (R2 = 0.9553(SDF/IDF), RMSE = 0.0464.) and identify optimal fermentation conditions. Under these optimal conditions, both strains exhibited synergistic effects, where the lactic acid produced by Lactobacillus bulgaricus and β-glucosidase produced by Kluyveromyces marxianus may facilitate IDF decomposition and conversion, resulting in a maximum SDF/IDF ratio of 0.6911. This led to a 27.65 % reduction in IDF content and a 19.11 % increase in SDF content. Moreover, the physicochemical and functional properties of DF were enhanced after co-fermentation. The structure of DF became looser and more porous, its thermal stability improved, and its water-holding, oil-holding, and swelling capacities increased by 53.54 %, 16.11 %, and 44.96 %, respectively, compared with the unfermented counterpart; in terms of adsorption characteristics, its glucose, cholesterol and nitrite adsorption capacities were also significantly improved. According to in vitro gastrointestinal simulated digestion, digestion would have a great impact on the fermented DF, which showed good antioxidant properties during the intestinal digestion stage.

Recent advances in dark fermentative hydrogen production from vegetable waste: role of inoculum, consolidated bioprocessing, and machine learning

Waste-centred-bioenergy generation have been garnering interest over the years due to environmental impact presented by fossil fuels. Waste generation is an unavoidable consequence of urbanization and population growth. Sustainable waste management techniques that are long term and environmentally benign are required to achieve sustainable development. Energy recovery from waste biomass via dark fermentative hydrogen production is a sustainable approach to waste management. Vegetable waste is generated in plenty over the food supply chain and being a rich source of carbon and other nutrients it has been studied for production of biohydrogen. This review aims to offer a comprehensive overview on the potential of vegetable waste as a feedstock for dark fermentative biohydrogen production. The hydrogen output from dark fermentative process is lower and additional strategies are required to improve the production. This review addresses the challenges generally encountered during dark fermentative hydrogen production using vegetable waste and the importance of methods such as bioaugmentation and application of extremophiles for process enhancement. The role of machine learning in the field of biohydrogen production is briefly discussed. The application of dark fermentative effluents for secondary valuable product generation and its contribution to the biohydrogen biorefinery is discussed as well.

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.

Anaerobic membrane bioreactors for municipal wastewater: Progress in resource and energy recovery improvement approaches

Anaerobic membrane bioreactor (AnMBR) offer promise in municipal wastewater treatment, with potential benefits including high-quality effluent, energy recovery, sludge reduction, and mitigating greenhouse gas emissions. However, AnMBR face hurdles like membrane fouling, low energy recovery, etc. In light of net-zero carbon target and circular economy strategy, this work sought to evaluate novel AnMBR configurations, focusing on performance, fouling mitigation, net-energy generation, and nutrients-enhancing integrated configurations, such as forward osmosis (FO), membrane distillation (MD), bioelectrochemical systems (BES), membrane photobioreactor (MPBR), and partial nitrification-anammox (PN/A). In addition, we highlight the essential role of AnMBR in advancing the circular economy and propose ideas for the water-energy-climate nexus. While AnMBR has made significant progress, challenges, such as fouling and cost-effectiveness persist. Overall, the use of novel configurations and energy recovery strategies can further improve the sustainability and efficiency of AnMBR systems, making them a promising technology for future sustainable municipal wastewater treatment.

Prediction of g-C3N4-based photocatalysts in tetracycline degradation based on machine learning

Investigating the effects of g-C3N4-based photocatalysts on experimental parameters during tetracycline (TC) degradation can be helpful in discovering the optimal parameter combinations to improve the degradation efficiencies in general. Machine learning methods can avoid the problems of high cost, time-consuming and possible instrumental errors in experimental methods, which have been proven to be an effective alternative for evaluating the entire experimental process. Eight typical machine learning models were explored for their effectiveness in predicting the TC degradation efficiencies of g-C3N4 based photocatalysts. XGBoost (XGB) was the most reliable model with R2, RMSE and MAE values of 0.985, 4.167 and 2.900, respectively. In addition, XGB's feature importance and SHAP method were used to rank the importance of features to provide interpretability to the results. This study provided a new idea for developing g-C3N4-based photocatalysts for TC degradation and intelligent algorithms for predicting the photocatalytic activity of g-C3N4-based photocatalysts.

Recent advancements in wastewater treatment via anaerobic fermentation process: A systematic review

This manuscript delves into the realm of wastewater treatment, with a particular emphasis on anaerobic fermentation processes, especially dark, photo, and dark-photo fermentation processes, which have not been covered and overviewed previously in the literature regarding the treatment of wastewater. Moreover, the study conducts a bibliometric analysis for the first time to elucidate the research landscape of anaerobic fermentation utilization in wastewater purification. Furthermore, microorganisms, ranging from microalgae to bacteria and fungi, emphasizing the integration of these agents for enhanced efficiency, are all discussed and compared. Various bioreactors, such as dark and photo fermentation bioreactors, including tubular photo bioreactors, are scrutinized for their design and operational intricacies. The results illustrated that using clostridium pasteurianum CH4 and Rhodopseudomonas palustris WP3-5 in a combined dark-photo fermentation process can treat wastewater to a pH of nearly 7 with over 90% COD removal. Also, integrating Chlorella sp and Activated sludge can potentially treat synthetic wastewater to COD, P, and N percentage removal rates of 99%,86%, and 79%, respectively. Finally, the paper extends to discuss the limitations and future prospects of dark-photo fermentation processes, offering insights into the road ahead for researchers and scientists.

Machine Learning Exploration of the Relationship Between Drugs and the Blood-Brain Barrier: Guiding Molecular Modification

OBJECTIVE

This study aimed to improve the efficiency of pharmacotherapy for CNS diseases by optimizing the ability of drug molecules to penetrate the Blood-Brain Barrier (BBB).

METHODS

We established qualitative and quantitative databases of the ADME properties of drugs and derived characteristic features of compounds with efficient BBB penetration. Using these insights, we developed four machine learning models to predict a drug's BBB permeability by assessing ADME properties and molecular topology. We then validated the models using the B3DB database. For acyclovir and ceftriaxone, we modified the Hydrogen Bond Donors and Acceptors, and evaluated the BBB permeability using the predictive model.

RESULTS

The machine learning models performed well in predicting BBB permeability on both internal and external validation sets. Reducing the number of Hydrogen Bond Donors and Acceptors generally improves BBB permeability. Modification only enhanced BBB penetration in the case of acyclovir and not ceftriaxone.

CONCLUSIONS

The machine learning models developed can accurately predict BBB permeability, and many drug molecules are likely to have increased BBB penetration if the number of Hydrogen Bond Donors and Acceptors are reduced. These findings suggest that molecular modifications can enhance the efficacy of CNS drugs and provide practical strategies for drug design and development. This is particularly relevant for improving drug penetration of the BBB.

Functional link hybrid artificial neural network for predicting continuous biohydrogen production in dynamic membrane bioreactor

Conventional machine learning approaches have shown limited predictive power when applied to continuous biohydrogen production due to nonlinearity and instability. This study was aimed at forecasting the dynamic membrane reactor performance in terms of the hydrogen production rate (HPR) and hydrogen yield (HY) using laboratory-based daily operation datapoints for twelve input variables. Hybrid algorithms were developed by integrating particle swarm optimized with functional link artificial neural network (PSO-FLN) which outperformed other hybrid algorithms for both HPR and HY, with determination coefficients (R2) of 0.97 and 0.80 and mean absolute percentage errors of 0.014 % and 0.023 %, respectively. Shapley additive explanations (SHAP) explained the two positive-influencing parameters, OLR_added (1.1-1.3 mol/L/d) and butyric acid (7.5-16.5 g COD/L) supports the highest HPR (40-60 L/L/d). This research indicates that PSO-FLN model are capable of handling complicated datasets with high precision in less computational timeat 9.8 sec for HPR and 10.0 sec for HY prediction.

Machine learning modeling and additive explanation techniques for glutathione production from multiple experimental growth conditions of Saccharomyces cerevisiae

Glutathione (GSH) production is of great industrial interest due to its essential properties. This study aimed to use machine learning (ML) methods to model GSHproduction under different growth conditions of Saccharomyces cerevisiae, namely cultivation time, culture volume, pressure, and magnetic field application. Different ML and regression models were evaluated for their statistics to select the most robust model. Results showed that eXtreme Gradient Boosting (XGB) was the best predictive performance model. From the best model, additive explanation techniques were used to identify the feature importance of process. According to variable analysis, the best conditions to obtain the highest GSH concentrations would be cultivation times of 72-96 h, low magnetic field intensity (3.02 mT), low pressure (0.5 kgf.cm-2), and high culture volume (3.5 L). XGB use and additive explanation techniques proved promising for determining process optimization conditions and selecting the essential process variables.

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.

Contribution of molecular structures and quantum chemistry technique to root concentration factor: An innovative application of interpretable machine learning

Root concentration factor (RCF) is a significant parameter to characterize uptake and accumulation of hazardous organic contaminants (HOCs) by plant roots. However, complex interactions among chemicals, plant roots and soil make it challenging to identify underlying mechanisms of uptake and accumulation of HOCs. Here, nine machine learning techniques were applied to investigate major factors controlling RCF based on variable combinations of molecular descriptors (MD), MACCS fingerprints, quantum chemistry descriptors (QCD) and three physicochemical properties related to chemical-soil-plant system. Compared to models with variables including MACCS fingerprints or solitary physicochemical properties, the XGBoost-6 model developed by the variable combination of MD, QCD and three physicochemical properties achieved the most remarkable performance, with R2 of 0.977. Model interpretation achieved by permutation variable importance and partial dependence plots revealed the vital importance of HOCs lipophilicity, lipid content of plant roots, soil organic matter content, the overall deformability and the molecular dispersive ability of HOCs for regulating RCF. The integration of MD and QCD with physicochemical properties could improve our knowledge of underlying mechanisms regarding HOCs accumulation in plant roots from innovative structural perspectives. Multiple variables combination-oriented performance improvement of model can be extended to other parameters prediction in environmental risk assessment field.

Prediction of composting maturity and identification of critical parameters for green waste compost using machine learning

Ensuring the maturity of green waste compost is crucial to composting processes and quality control of compost products. However, accurate prediction of green waste compost maturity remains a challenge, as there are limited computational methods available. This study aimed to address this issue by employing four machine learning models to predict two indicators of green waste compost maturity: seed germination index (GI) and T value. The four models were compared, and the Extra Trees algorithm exhibited the highest prediction accuracy with R2 values of 0.928 for GI and 0.957 for T value. To identify the interactions between critical parameters and compost maturity, The Pearson correlation matrix and Shapley Additive exPlanations (SHAP) analysis were conducted. Furthermore, the accuracy of the models was validated through compost validation experiments. These findings highlight the potential of applying machine learning algorithms to predict green waste compost maturity and optimise process regulation.

Effects of heavy metal exposure on hypertension: A machine learning modeling approach

Heavy metal exposure is a common risk factor for hypertension. To develop an interpretable predictive machine learning (ML) model for hypertension based on levels of heavy metal exposure, data from the NHANES (2003-2016) were employed. Random forest (RF), support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), ridge regression (RR), AdaBoost (AB), gradient boosting decision tree (GBDT), voting classifier (VC), and K-nearest neighbour (KNN) algorithms were utilized to generate an optimal predictive model for hypertension. Three interpretable methods, the permutation feature importance analysis, partial dependence plot (PDP), and Shapley additive explanations (SHAP) methods, were integrated into a pipeline and embedded in ML for model interpretation. A total of 9005 eligible individuals were randomly allocated into two distinct sets for predictive model training and validation. The results showed that among the predictive models, the RF model demonstrated the highest performance, achieving an accuracy rate of 77.40% in the validation set. The AUC and F1 score for the model were 0.84 and 0.76, respectively. Blood Pb, urinary Cd, urinary Tl, and urinary Co levels were identified as the main influencers of hypertension, and their contribution weights were 0.0504 ± 0.0482, 0.0389 ± 0.0256, 0.0307 ± 0.0179, and 0.0296 ± 0.0162, respectively. Blood Pb (0.55-2.93 μg/dL) and urinary Cd (0.06-0.15 μg/L) levels exhibited the most pronounced upwards trend with the risk of hypertension within a specific value range, while urinary Tl (0.06-0.26 μg/L) and urinary Co (0.02-0.32 μg/L) levels demonstrated a declining trend with hypertension. The findings on the synergistic effects indicated that Pb and Cd were the primary determinants of hypertension. Our findings underscore the predictive value of heavy metals for hypertension. By utilizing interpretable methods, we discerned that Pb, Cd, Tl, and Co emerged as noteworthy contributors within the predictive model.

Modeling and optimization of photo-fermentation biohydrogen production from co-substrates basing on response surface methodology and artificial neural network integrated genetic algorithm

The main aim of the present study was to establish a relationship model between bio-hydrogen yield and the key operating parameters affecting photo-fermentation hydrogen production (PFHP) from co-substrates. Central composite design-response surface methodology (CCD-RSM) and artificial neural network-genetic algorithm (ANN-GA) models were used to optimize the hydrogen production performance from co-substrates. Compared to CCD-RSM, the ANN-GA had higher determination coefficient (R² = 0.9785) and lower mean square error (MSE = 9.87), average percentage deviation (APD = 2.72) and error (4.3%), indicating the ANN-GA was more suitable, reliable and accurate in predicting biohydrogen yield from co-substrates by PFHP. The highest biohydrogen yield (99.09 mL/g) predicted by the ANN-GA model at substrate concentration 35.62 g/L, temperature 30.94 °C, initial pH 7.49 and inoculation ratio 32.98 %(v/v), which was 4.20 % higher than the CCD-RSM model (95.10 mL/g).

A Review Unveiling Various Machine Learning Algorithms Adopted for Biohydrogen Productions from Microalgae

Biohydrogen production from microalgae is a potential alternative energy source that is now intensively being researched. The complex natures of the biological processes involved have afflicted the accuracy of traditional modelling and optimization, besides being costly. Accordingly, machine learning algorithms have been employed to overcome setbacks, as these approaches have the capability to predict nonlinear interactions and handle multivariate data from microalgal biohydrogen studies. Thus, the review focuses on revealing the recent applications of machine learning techniques in microalgal biohydrogen production. The working principles of random forests, artificial neural networks, support vector machines, and regression algorithms are covered. The applications of these techniques are analyzed and compared for their effectiveness, advantages and disadvantages in the relationship studies, classification of results, and prediction of microalgal hydrogen production. These techniques have shown great performance despite limited data sets that are complex and nonlinear. However, the current techniques are still susceptible to overfitting, which could potentially reduce prediction performance. These could be potentially resolved or mitigated by comparing the methods, should the input data be limited.

Review on machine learning-based bioprocess optimization, monitoring, and control systems

Machine Learning is quickly becoming an impending game changer for transforming big data thrust from the bioprocessing industry into actionable output. However, the complex data set from bioprocess, lagging cyber-integrated sensor system, and issues with storage scalability limit machine learning real-time application. Hence, it is imperative to know the state of technology to address prevailing issues. This review first gives an insight into the basic understanding of the machine learning domain and discusses its complexities for more comprehensive applications. Followed by an outline of how relevant machine learning models are for statistical and logical analysis of the enormous datasets generated to control bioprocess operations. Then this review critically discusses the current knowledge, its limitations, and future aspects in different subfields of the bioprocessing industry. Further, this review discusses the prospects of adopting a hybrid method to dovetail different modeling strategies, cyber-networking, and integrated sensors to develop new digital biotechnologies.

Value addition through biohydrogen production and integrated processes from hydrothermal pretreatment of lignocellulosic biomass

Bioenergy production is the most sought-after topics at the crunch of energy demand, climate change and waste generation. In view of this, lignocellulosic biomass (LCB) rich in complex organic content has the potential to produce bioenergy in several forms following the pretreatment. Hydrothermal pretreatment that employs high temperatures and pressures is gaining momentum for organics recovery from LCB which can attain value-addition. Diverse bioprocesses such as dark fermentation, anaerobic digestion etc. can be utilized following the pretreatment of LCB which can result in biohydrogen and biomethane production. Besides, integration approaches for LCB utilization that enhance process efficiency and additional products such as biohythane production as well as application of solid residue obtained after LCB pretreatment were discussed. Importance of hydrothermal pretreatment as one of the suitable strategies for LCB utilization is emphasized suggesting its future potential in large scale energy recovery.

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.

Artificial intelligence technologies in bioprocess: Opportunities and challenges

Bioprocess control and optimization are crucial for tapping the metabolic potential of microorganisms, and which have made great progress in the past decades. Combination of the current control and optimization technologies with the latest computer-based strategies will be a worth expecting way to improve bioprocess further. Recently, artificial intelligence (AI) emerged as a data-driven technique independent of the complex interactions used in mathematical models and has been gradually applied in bioprocess. In this review, firstly, AI-guided modeling approaches of bioprocess are discussed, which are widely applied to optimize critical process parameters (CPPs). Then, AI-assisted rapid detection and monitoring technologies employed in bioprocess are summarized. Next, control strategies according to the above two technologies in bioprocess are analyzed. Lastly, current research gaps and future perspectives on AI-guided optimization and control technologies are discussed. This review provides theoretical guidance for developing AI-guided bioprocess optimization and control technologies.

Using machine learning models to predict the effects of seasonal fluxes on Plesiomonas shigelloides population density

Seasonal variations (SVs) affect the population density (PD), fate, and fitness of pathogens in environmental water resources and the public health impacts. Therefore, this study is aimed at applying machine learning intelligence (MLI) to predict the impacts of SVs on P. shigelloides population density (PDP) in the aquatic milieu. Physicochemical events (PEs) and PDP from three rivers acquired via standard microbiological and instrumental techniques across seasons were fitted to MLI algorithms (linear regression (LR), multiple linear regression (MR), random forest (RF), gradient boosted machine (GBM), neural network (NN), K-nearest neighbour (KNN), boosted regression tree (BRT), extreme gradient boosting (XGB) regression, support vector regression (SVR), decision tree regression (DTR), M5 pruned regression (M5P), artificial neural network (ANN) regression (with one 10-node hidden layer (ANN10), two 6- and 4-node hidden layers (ANN64), and two 5- and 5-node hidden layers (ANN55)), and elastic net regression (ENR)) to assess the implications of the SVs of PEs on aquatic PDP. The results showed that SVs significantly influenced PDP and PEs in the water (p < 0.0001), exhibiting a site-specific pattern. While MLI algorithms predicted PDP with differing absolute flux magnitudes for the contributing variables, DTR predicted the highest PDP value of 1.707 log unit, followed by XGB (1.637 log unit), but XGB (mean-squared-error (MSE) = 0.0025; root-mean-squared-error (RMSE) = 0.0501; R² =0.998; medium absolute deviation (MAD) = 0.0275) outperformed other models in terms of regression metrics. Temperature and total suspended solids (TSS) ranked first and second as significant factors in predicting PDP in 53.3% (8/15) and 40% (6/15), respectively, of the models, based on the RMSE loss after permutations. Additionally, season ranked third among the 7 models, and turbidity (TBS) ranked fourth at 26.7% (4/15), as the primary significant factor for predicting PDP in the aquatic milieu. The results of this investigation demonstrated that MLI predictive modelling techniques can promisingly be exploited to complement the repetitive laboratory-based monitoring of PDP and other pathogens, especially in low-resource settings, in response to seasonal fluxes and can provide insights into the potential public health risks of emerging pathogens and TSS pollution (e.g., nanoparticles and micro- and nanoplastics) in the aquatic milieu. The model outputs provide low-cost and effective early warning information to assist watershed managers and fish farmers in making appropriate decisions about water resource protection, aquaculture management, and sustainable public health protection.

Machine learning estimation of biodegradable organic matter concentrations in municipal wastewater

This study investigates whether a novel estimation method based on machine learning can feasibly predict the readily biodegradable chemical oxygen demand (RB-COD) and slowly biodegradable COD (SB-COD) in municipal wastewater from the oxidation-reduction potential (ORP) data of anoxic batch experiments. Anoxic batch experiments were conducted with highly mixed liquor volatile suspended solids under different RB-COD and SB-COD conditions. As the RB-COD increased, the ORP breakpoint appeared earlier, and fermentation occurred in the interior of the activated sludge, even under anoxic conditions. Therefore, the ORP decline rates before and after the breakpoint were significantly correlated with the RB-COD and SB-COD, respectively (p < 0.05). The two biodegradable CODs were estimated separately using six machine learning models: an artificial neural network (ANN), support vector regression (SVR), an ANN-based AdaBoost, a SVR-based AdaBoost, decision tree, and random forest. Against the ORP dataset, the RB-COD and SB-COD estimation correlation coefficients of SVR-based AdaBoost were 0.96 and 0.88, respectively. To identify which ORP data are useful for estimations, the ORP decline rates before and after the breakpoint were separately input as datasets to the estimation methods. All six machine learning models successfully estimated the two biodegradable CODs simultaneously with accuracies of ≥0.80 from only ORP time-series data. Sensitivity analysis using the Shapley additive explanation method demonstrated that the ORP decline rates before and after the breakpoint obviously contributed to the estimation of RB-COD and SB-COD, respectively, indicating that acquiring the ORP data with various decline rates before and after the breakpoint improved the estimations of RB-COD and SB-COD, respectively. This novel estimation method for RB-COD and SB-COD can assist the rapid control of biological wastewater treatment when the biodegradable organic matter concentration dynamically changes in influent wastewater.

Machine learning technology in biohydrogen production from agriculture waste: Recent advances and future perspectives

Agricultural waste biomass has shown great potential to deliver green energy produced by biochemical and thermochemical conversion processes to mitigate future energy crises. Biohydrogen has become more interested in carbon-free and high-energy dense fuels among different biofuels. However, it is challenging to develop models based on experience or theory for precise predictions due to the complexity of biohydrogen production systems and the limitations of human perception. Recent advancements in machine learning (ML) may open up new possibilities. For this reason, this critical study offers a thorough understanding of ML's use in biohydrogen production. The most recent developments in ML-assisted biohydrogen technologies, including biochemical and thermochemical processes, are examined in depth. This review paper also discusses the prediction of biohydrogen production from agricultural waste. Finally, the techno-economic and scientific obstacles to ML application in agriculture waste biomass-based biohydrogen production are summarized.

Computational fluid dynamics simulation study of hypersaline water desalination via membrane distillation: Effect of membrane characteristics and operational parameters

In this study, a comprehensive model was developed using Computational Fluid Dynamics (CFD), and the behaviour of a direct contact membrane distillation (DCMD) system was investigated at hypersaline feedwater conditions. The effects of various operating parameters including feed and permeate velocities, temperatures and salinities, as well as different membrane characteristics like thickness, porosity, and thermal conductivity were studied. The developed simulation model was also validated using experimental data. The results showed that the membrane conductivity and thickness had a significant impact on the DCMD performance, and the optimum operational condition was necessary to be determined. The results showed that increasing the feedwater salinity from 50 to 200 g/l decreased the membrane flux by up to 33%, while a four times decrease in thermal conductivity of the membrane could lead to an increase in the membrane flux from 11.2 to 32.4 l/m²·h (LMH). In addition, the optimal membrane thickness was found to increase with salinity, reaching >120 μm for treatment of 22 wt% NaCl feedwater solution. However, the flux declined from >32 LMH to <13 LMH upon the increase in feedwater salinity (up to 22 wt% NaCl solution). It is also shown that a thinner membrane performed better for desalination of low salinity feedwater, while the thicker one produces higher separation performance and thermal efficiency for hypersaline brine desalination.

Bio-hydrogen production by dark anaerobic fermentation of organic wastewater

Using organic wastewater to produce hydrogen by fermentation can generate clean energy while treating wastewater. At present, there are many inhibitory factors in the hydrogen production process, resulting in unsatisfactory hydrogen yield and hydrogen concentration during the fermentation process, and there are still great obstacles to the industrial promotion and commercial application of organic wastewater fermentation hydrogen production. This paper summarizes the hydrogen production of organic wastewater dark anaerobic fermentation technology. The current anaerobic fermentation hydrogen production systems and technologies are summarized and compared, and the factors and potential conditions that affect the performance of hydrogen production are discussed. The further requirements and research priorities for the market application of fermentation biohydrogen production technology in wastewater utilization are prospected.

A Superimposed QD-Based Optical Antenna for VLC: White LED Source

Visible light communication (VLC) is a versatile enabling technology for following high-speed wireless communication because of its broad unlicensed spectrum. In this perspective, white light-emitting diodes (LED) provide both illumination and data transmission simultaneously. To accomplish a VLC system, receiver antennas play a crucial role in receiving light signals and guiding them toward a photodetector to be converted into electrical signals. This paper demonstrates an optical receiver antenna based on luminescent solar concentrator (LSC) technology to exceed the conservation of etendue and reach a high signal-to-noise ratio. This optical antenna is compatible with all colors of LEDs and achieves an optical efficiency of 3.75%, which is considerably higher than the similar reported antenna. This antenna is fast due to the small attached photodetector—small enough that it can be adapted for electronic devices—which does not need any tracking system. Moreover, numerical simulation is performed using a Monte Carlo ray-tracing model, and results are extracted in the spectral domain. Finally, the fate of each photon and the chromaticity diagram of the collected photons’ spectra are specified.

Development of a wide-range soft sensor for predicting wastewater BOD5 using an eXtreme gradient boosting (XGBoost) machine

In wastewater monitoring, detecting extremely high pollutant concentrations is necessary to properly calibrate the treatment process. However, existing hardware sensors have a limited linear range which may fail to measure extremely high levels of pollutants; and likewise, the conventional "soft" model sensors are not suitable for the highly-skewed data distributions either. This study developed a new soft sensor by using eXtreme Gradient Boosting (XGBoost) machine learning to 'measure' the wastewater organics (in terms of 5-day biochemical oxygen demand (BOD5)). The soft sensor was tested on influent and effluent BOD5 of two different wastewater treatment plants to validate the results. The model results showed that XGBoost can detect these extreme values better than conventional soft sensors. This new soft sensor can function using a sparse input matrix via XGBoost's sparsity awareness algorithm - which can address the limitation of the conventional soft sensor with the fallibility of supporting hardware sensors even.

Feature Selection to Predict LED Light Energy Consumption with Specific Light Recipes in Closed Plant Production Systems

The use of closed growth environments, such as greenhouses, plant factories, and vertical farms, represents a sustainable alternative for fresh food production. Closed plant production systems (CPPSs) allow growing of any plant variety, no matter the year’s season. Artificial lighting plays an essential role in CPPSs as it promotes growth by providing optimal conditions for plant development. Nevertheless, it is a model with a high demand for electricity, which is required for artificial radiation systems to enhance the developing plants. A high percentage (40% to 50%) of the costs in CPPSs point to artificial lighting systems. Due to this, lighting strategies are essential to improve sustainability and profitability in closed plant production systems. However, no tools have been applied in the literature to contribute to energy savings in LED-type artificial radiation systems through the configuration of light recipes (wavelengths combination. For CPPS to be cost-effective and sustainable, a pre-evaluation of energy consumption for plant cultivation must consider. Artificial intelligence (AI) methods integrated into the prediction crucial variables such as each input-variable light color or specific wavelengths like red, green, blue, and white along with light intensity (quantity), frequency (pulsed light), and duty cycle. This paper focuses on the feature-selection stage, in which a regression model is trained to predict energy consumption in LED lights with specific light recipes in CPPSs. This stage is critical because it identifies the most representative features for training the model, and the other stages depend on it. These tools can enable further in-depth analysis of the energy savings that can be obtained with light recipes and pulsed and continuous operation light modes in artificial LED lighting systems.

A Proposal for Optical Antenna in VLC Communication Receiver System

Visible Light Communication (VLC) is an important emerging choice for high-speed wireless communication. In this perspective, light-emitting diodes as illuminators will be modulated to transmit data simultaneously. However, the receivers bring severe difficulties due to cost, response time, and sensitivity with a wide Field Of View (FOV). To avoid these problems, one approach is to apply a large area photodetector; however, this solution is slow and costly. Another method is to focus light on a fast photodetector by optical components, but the photodetector’s FOV decreases, resulting from the conservation of etendue. Another option is Luminescent Solar Concentrators (LSCs). This paper demonstrates a novel shape of LSC with advantages such as inexpensive, fast response time, small antenna area for VLC purposes with significant geometrical gain, FOV, and ultra-broad bandwidth. It does not require any complex tracking system and active pointing but, due to its tiny size, it can also be adapted in integrating and mobile devices. Numerical simulation is done using Monte-Carlo raytracing, and the results are demonstrated in the spectral domain. The optical efficiency of the proposed antenna is obtained at 1.058%, which is about 0.4% better than the efficiency levels reported in other works, and the geometric gain of the antenna is reported to be 44, which is significant.

Prediction of heterogeneous Fenton process in treatment of melanoidin-containing wastewater using data-based models

Predictive capability of response surface methodology (RSM) and ant colony optimization combined with support vector regression (ACO-SVR) models are applied for determining optimal parameters in the process of heterogeneous Fenton oxidation of melanoidin, a high molecular weight polymer widely produced during fermentation processes generating large quantities of wastewater with intense brown color and extremely high chemical oxygen demand (COD). Prediction of the performance of nano zero-valent iron supported on activated carbon cloth-chitosan (ACC-CH-nZVI) catalysts was carried out using Box-Behnken design (BBD) and analysis of variance to evaluate the interaction of independent variables involved in heterogeneous Fenton reaction. The optimized condition with minimal consumption of H₂O₂ (173 mM) resulted in 77.1% decolorization of melanoidin-contaminated water corresponding to 74.4% COD removal at pH 3 (600 mg/l Fe dosage) for 90 min reaction time. The corresponding weight ratio of H₂O₂ to COD was 0.98, much lower than the stoichiometric value 2.125, indicating the effectiveness of ACC-CH-nZVI as a heterogeneous Fenton-like catalyst. In comparison to previously published experimental results, ACO-SVR model shows higher coefficient of determination (R²; 0.9983) but lower root mean squared error (RMSE) and mean absolute error (MAE) than those of RSM model, indicating relative superiority in prediction capability. Besides, ACO algorithm appears to be a promising tool for improving forecasting accuracy of SVR model. This work demonstrates the applicability of ACO-SVR model in predicting the performance of wastewater treatment using Fenton process with limited number of experiment and exhibits satisfactory prediction results.