SVM and ANN Modelling Approach for the Optimization of Membrane Permeability of a Membrane Rotating Biological Contactor for Wastewater Treatment

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.

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.

A Novel Modeling Optimization Approach for a Seven-Channel Titania Ceramic Membrane in an Oily Wastewater Filtration System Based on Experimentation, Full Factorial Design, and Machine Learning

This comprehensive study looks at how operational conditions affect the performance of a novel seven-channel titania ceramic ultrafiltration membrane for the treatment of produced water. A full factorial design experiment (23) was conducted to study the effect of the cross-flow operating factors on the membrane permeate flux decline and the overall permeate volume. Eleven experimental runs were performed for three important process operating variables: transmembrane pressure (TMP), crossflow velocity (CFV), and filtration time (FT). Steady final membrane fluxes and permeate volumes were recorded for each experimental run. Under the optimized conditions (1.5 bar, 1 m/s, and 2 h), the membrane performance index demonstrated an oil rejection rate of 99%, a flux of 297 L/m2·h (LMH), a 38% overall initial flux decline, and a total permeate volume of 8.14 L. The regression models used for the steady-state membrane permeate flux decline and overall permeate volume led to the highest goodness of fit to the experimental data with a correlation coefficient of 0.999. A Multiple Linear Regression method and an Artificial Neural Network approach were also employed to model the experimental membrane permeate flux decline and analyze the impact of the operating conditions on membrane performance. The predictions of the Gaussian regression and the Levenberg-Marquardt backpropagation method were validated with a determination coefficient of 99% and a Mean Square Error of 0.07.

Optimization of Operational Parameters Using Artificial Neural Network and Support Vector Machine for Bio-oil Extracted from Rice Husk

Bio-oil production from rice husk, an abundant agricultural residue, has gained significant attention as a sustainable and renewable energy source. The current research aims to employ artificial neural network (ANN) and support vector machine (SVM) modeling techniques for the optimization of operating parameters for bio-oil extracted from rice husk ash (RHA) through pyrolysis. ANN and SVM methods are employed to model and optimize the operational conditions, including temperature, heating rate, and feedstock particle size, to enhance the yield and quality of bio-oil. Additionally, ANN modeling is utilized to create a predictive model for bio-oil properties, allowing for the efficient optimization of pyrolysis conditions. This research provides valuable insights into the production and properties of bio-oil from RHA. By harnessing the capabilities of ANN and SVM, this research not only aids in understanding the intricate relationships between process variables and bio-oil properties but also provides a means to systematically enhance the production process. The predictive results obtained from the ANN were found to be good when compared with the SVM. Several models with different numbers of neurons have been trained with different transfer functions. R values for the training, validation, and test phases are around 1.0, i.e., 0.9981, 0.9976, and 0.9978, respectively. The overall R-value was 0.9960 for the proposed network. The findings were considered acceptable, as the overall R-value was close to 1.0. The optimized operational parameters contribute to the efficient conversion of RHA into bio-oil, thereby promoting the use of this sustainable resource for renewable energy production. This approach aligns with the growing emphasis on reducing the environmental impact of traditional fossil fuels and advancing the utilization of alternative and environmentally friendly energy sources.

Artificial intelligence and machine learning algorithms in the detection of heavy metals in water and wastewater: Methodological and ethical challenges

Heavy metals (HMs) enter waterbodies through various means, which, when exceeding a threshold limit, cause toxic effects both on the environment and in humans upon entering their systems. Recent times have seen an increase in such HM influx incident rates. This requires an instant response in this regard to review the challenges in the available classical methods for HM detection and removal. As well as provide an opportunity to explore the applications of artificial intelligence (AI) and machine learning (ML) for the identification and further redemption of water and wastewater from the HMs. This review of research focuses on such applications in conjunction with the available in-silico models producing worldwide data for HM levels. Furthermore, the effect of HMs on various disease progressions has been provided, along with a brief account of prediction models analysing the health impact of HM intoxication. Also discussing the ethical and other challenges associated with the use of AI and ML in this field is the futuristic approach intended to follow, opening a wide scope of possibilities for improvement in wastewater treatment methodologies.

Optimization of operational parameters using RSM, ANN, and SVM in membrane integrated with rotating biological contactor

Membrane fouling is a critical bottleneck to the widespread adoption of membrane separation processes. It diminishes the membrane permeability and results in high operational energy costs. The current study presents optimizing the operating parameters of a novel rotating biological contactor (RBC) integrated with an external membrane (RBC + ME) that combines membrane technology with an RBC. In the RBC + ME, the membrane panel is placed external to the bioreactor. Response surface methodology (RSM) is applied to optimize the membrane permeability through three operating parameters (hydraulic retention time (HRT), rotational disk speed, and sludge retention time (SRT)). The artificial neural networks (ANN) and support vector machine (SVM) are implemented to depict the statistical modelling approach using experimental data sets. The results showed that all three operating parameters contribute significantly to the performance of the bioreactor. RSM revealed an optimum value of 40.7 rpm disk rotational speed, 18 h HRT and 12.4 d SRT, respectively. An ANN model with ten hidden layers provides the highest R2 value, while the SVM model with the Bayesian optimizer provides the highest R2. RSM, ANN, and SVM models reveal the highest R-square values of 0.97, 0.99, and 0.99, respectively. Machine learning techniques help predict the model based on the experimental results and training data sets.

A comprehensive review on artificial intelligence in water treatment for optimization. Clean water now and the future

Given the severe effects that toxic compounds present in wastewater streams have on humans, it is imperative that water and wastewater streams pollution be addressed globally. This review comprehensively examines various water and wastewater treatment methods and water quality management methods based on artificial intelligence (AI). Machine learning (ML) and AI have become a powerful tool for addressing problems in the real world and has gained a lot of interest since it can be used for a wide range of activities. The foundation of ML techniques involves training of a network with collected data, followed by application of learned network to the process simulation and prediction. The creation of ML models for process simulations requires measured data. In order to forecast and simulate chemical and physical processes such chemical reactions, heat transfer, mass transfer, energy, pharmaceutics and separation, a variety of machine-learning algorithms have recently been developed. These models have shown to be more adept at simulating and modeling processes than traditional models. Although AI offers many advantages, a number of disadvantages have kept these methods from being extensively applied in actual water treatment systems. Lack of evidence of application in actual water treatment scenarios, poor repeatability and data availability and selection are a few of the main problems that need to be resolved.

Effect of Operating Parameters on the Performance of Integrated Fixed-Film Activated Sludge for Wastewater Treatment

Integrated fixed-film activated sludge (IFAS) is a hybrid wastewater treatment process that combines suspended and attached growth. The current review provides an overview of the effect of operating parameters on the performance of IFAS and their implications for wastewater treatment. The operating parameters examined include hydraulic retention time (HRT), solids retention time (SRT), dissolved oxygen (DO) levels, temperature, nutrient loading rates, and aeration. Proper control and optimization of these parameters significantly enhance the treatment efficiency and pollutant removal. Longer HRT and appropriate SRT contribute to improved organic matter and nutrient removal. DO levels promote the growth of aerobic microorganisms, leading to enhanced organic matter degradation. Temperature influences microbial activity and enzymatic reactions, impacting treatment efficiency. Nutrient loading rates must be carefully managed to avoid system overload or inhibition. Effective aeration ensures uniform distribution of wastewater and biofilm carriers, optimizing contact between microorganisms and pollutants. IFAS has been used in water reuse applications, providing a sustainable and reliable water source for non-potable uses. Overall, IFAS has proven to be an effective and efficient treatment process that can provide high-quality effluent suitable for discharge or reuse. Understanding the effects of these operating parameters helps to optimize the design and operation for efficient wastewater treatment. Further research is needed to explore the interactions between different parameters, evaluate their impact under varying wastewater characteristics, and develop advanced control strategies for improved performance and sustainability.

Modeling the CO2 separation capability of poly(4-methyl-1-pentane) membrane modified with different nanoparticles by artificial neural networks

Membranes are a potential technology to reduce energy consumption as well as environmental challenges considering the separation processes. A new class of this technology, namely mixed matrix membrane (MMM) can be fabricated by dispersing solid substances in a polymeric medium. In this way, the poly(4-methyl-1-pentene)-based MMMs have attracted great attention to capturing carbon dioxide (CO2), which is an environmental pollutant with a greenhouse effect. The CO2 permeability in different MMMs constituted of poly(4-methyl-1-pentene) (PMP) and nanoparticles was comprehensively analyzed from the experimental point of view. In addition, a straightforward mathematical model is necessary to compute the CO2 permeability before constructing the related PMP-based separation process. Hence, the current study employs multilayer perceptron artificial neural networks (MLP-ANN) to relate the CO2 permeability in PMP/nanoparticle MMMs to the membrane composition (additive type and dose) and pressure. Accordingly, the effect of these independent variables on CO2 permeability in PMP-based membranes is explored using multiple linear regression analysis. It was figured out that the CO2 permeability has a direct relationship with all independent variables, while the nanoparticle dose is the strongest one. The MLP-ANN structural features have efficiently demonstrated an appealing potential to achieve the highest accurate prediction for CO2 permeability. A two-layer MLP-ANN with the 3-8-1 topology trained by the Bayesian regulation algorithm is identified as the best model for the considered problem. This model simulates 112 experimentally measured CO2 permeability in PMP/ZnO, PMP/Al2O3, PMP/TiO2, and PMP/TiO2-NT with an excellent absolute average relative deviation (AARD) of lower than 5.5%, mean absolute error (MAE) of 6.87 and correlation coefficient (R) of higher than 0.99470. It was found that the mixed matrix membrane constituted of PMP and TiO2-NT (functionalized nanotube with titanium dioxide) is the best medium for CO2 separation.

The roles of artificial intelligence techniques for increasing the prediction performance of important parameters and their optimization in membrane processes: A systematic review

Membrane-based separation processes has been recently of significant global interest compared to other conventional separation approaches due to possessing undeniable advantages like superior performance, environmentally-benign nature and simplicity of application. Computational simulation of fluids has shown its undeniable role in modeling and simulation of numerous physical/chemical phenomena including chemical engineering, chemical reaction, aerodynamics, drug delivery and plasma physics. Definition of fluids can be occurred using the Navier-Stokes equations, but solving the equations remains an important challenge. In membrane-based separation processes, true perception of fluid's manner through disparate membrane modules is an important concern, which has been significantly limited applying numerical/computational procedures such s computational fluid dynamics (CFD). Despite this noteworthy advantage, the optimization of membrane processes using CFD is time-consuming and expensive. Therefore, combination of artificial intelligence (AI) and CFD can result in the creation of a promising hybrid model to accurately predict the model results and appropriately optimize membrane processes and phase separation. This paper aims to provide a comprehensive overview about the advantages of commonly-employed ML-based techniques in combination with the CFD to intelligently increase the optimization accuracy and predict mass transfer and the unfavorable events (i.e., fouling) in various membrane processes. To reach this objective, four principal strategies of AI including SL, USL, SSL and ANN were explained and their advantages/disadvantages were discussed. Then after, prevalent ML-based algorithm for membrane-based separation processes. Finally, the application potential of AI techniques in different membrane processes (i.e., fouling control, desalination and wastewater treatment) were presented.

Response Surface Methodology for the Synthesis and Characterization of Bio-Oil Extracted from Biomass Waste and Upgradation Using the Rice Husk Ash Catalyst

Rice husk ash (RHA), a low-cost biomaterial, was utilized to form bio-oil from pyrolysis in a batch-stirred reactor, followed by its upgradation using the RHA catalyst. In the present study, the effect of temperature (ranging from 400 to 480 °C) on bio-oil production produced from RHA was studied to obtain the maximum bio-oil yield. Response surface methodology (RSM) was applied to investigate the effect of operational parameters (temperature, heating rate, and particle size) on the bio-oil yield. The results showed that a maximum bio-oil output of 20.33% was obtained at 480 °C temperature, 80 °C/min heating rate, and 200 μm particle size. Temperature and heating rate positively impact the bio-oil yield, while particle size has little effect. The overall R² value of 0.9614 for the proposed model proved in good agreement with the experimental data. The physical properties of raw bio-oil were determined, and 1030 kg/m³ density, 12 MJ/kg calorific value, 1.40 cSt viscosity, 3 pH, and 72 mg KOH/g acid value were obtained, respectively. To enhance the characteristics of the bio-oil, upgradation was performed using the RHA catalyst through the esterification process. The upgraded bio-oil stemmed from a density of 0.98 g/cm³, an acid value of 58 mg of KOH/g, a calorific value of 16 MJ/kg, and a viscosity 10.5 cSt, respectively. The physical properties, GC-MS and FTIR, showed an improvement in the bio-oil characterization. The findings of this study indicate that RHA can be used as an alternative source for bio-oil production to create a more sustainable and cleaner environment.

Simulating and Comparing CO2/CH4 Separation Performance of Membrane-Zeolite Contactors by Cascade Neural Networks

Separating carbon dioxide (CO₂) from gaseous streams released into the atmosphere is becoming critical due to its greenhouse effect. Membrane technology is one of the promising technologies for CO₂ capture. SAPO-34 filler was incorporated in polymeric media to synthesize mixed matrix membrane (MMM) and enhance the CO₂ separation performance of this process. Despite relatively extensive experimental studies, there are limited studies that cover the modeling aspects of CO₂ capture by MMMs. This research applies a special type of machine learning modeling scenario, namely, cascade neural networks (CNN), to simulate as well as compare the CO₂/CH₄ selectivity of a wide range of MMMs containing SAPO-34 zeolite. A combination of trial-and-error analysis and statistical accuracy monitoring has been applied to fine-tune the CNN topology. It was found that the CNN with a 4-11-1 topology has the highest accuracy for the modeling of the considered task. The designed CNN model is able to precisely predict the CO₂/CH₄ selectivity of seven different MMMs in a broad range of filler concentrations, pressures, and temperatures. The model predicts 118 actual measurements of CO₂/CH₄ selectivity with an outstanding accuracy (i.e., AARD = 2.92%, MSE = 1.55, R = 0.9964).