Quantification of interfacial energies associated with membrane fouling in a membrane bioreactor by using BP and GRNN artificial neural networks

Research Progress of Machine Learning in Extending and Regulating the Shelf Life of Fruits and Vegetables

Fruits and vegetables are valued for their flavor and high nutritional content, but their perishability and seasonality present challenges for storage and marketing. To address these, it is essential to accurately monitor their quality and predict shelf life. Unlike traditional methods, machine learning efficiently handles large datasets, identifies complex patterns, and builds predictive models to estimate food shelf life. These models can be continuously refined with new data, improving accuracy and robustness over time. This article discusses key machine learning methods for predicting shelf life and quality control of fruits and vegetables, with a focus on storage conditions, physicochemical properties, and non-destructive testing. It emphasizes advances such as dataset expansion, model optimization, multi-model fusion, and integration of deep learning and non-destructive testing. These developments aim to reduce resource waste, provide theoretical basis and technical guidance for the formation of modern intelligent agricultural supply chains, promote sustainable green development of the food industry, and foster interdisciplinary integration in the field of artificial intelligence.

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.

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.

Treatment of volatile organic compounds and other waste gases using membrane biofilm reactors: A review on recent advancements and challenges

This article provides a comprehensive review of membrane biofilm reactors for waste gas (MBRWG) treatment, focusing on studies conducted since 2000. The first section discusses the membrane materials, structure, and mass transfer mechanism employed in MBRWG. The concept of a partial counter-diffusion biofilm in MBRWG is introduced, with identification of the most metabolically active region. Subsequently, the effectiveness of these biofilm reactors in treating single and mixed pollutants is examined. The phenomenon of membrane fouling in MBRWG is characterized, alongside an analysis of contributory factors. Furthermore, a comparison is made between membrane biofilm reactors and conventional biological treatment technologies, highlighting their respective advantages and disadvantages. It is evident that the treatment of hydrophobic gases and their resistance to volatility warrant further investigation. In addition, the emergence of the smart industry and its integration with other processes have opened up new opportunities for the utilization of MBRWG. Overcoming membrane fouling and developing stable and cost-effective membrane materials are essential factors for successful engineering applications of MBRWG. Moreover, it is worth exploring the mechanisms of co-metabolism in MBRWG and the potential for altering biofilm community structures.

A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs)

Membrane fouling is a major hurdle to effective pressure-driven membrane processes, such as microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), and reverse osmosis (RO). Fouling refers to the accumulation of particles, organic and inorganic matter, and microbial cells on the membrane's external and internal surface, which reduces the permeate flux and increases the needed transmembrane pressure. Various factors affect membrane fouling, including feed water quality, membrane characteristics, operating conditions, and cleaning protocols. Several models have been developed to predict membrane fouling in pressure-driven processes. These models can be divided into traditional empirical, mechanistic, and artificial intelligence (AI)-based models. Artificial neural networks (ANNs) are powerful tools for nonlinear mapping and prediction, and they can capture complex relationships between input and output variables. In membrane fouling prediction, ANNs can be trained using historical data to predict the fouling rate or other fouling-related parameters based on the process parameters. This review addresses the pertinent literature about using ANNs for membrane fouling prediction. Specifically, complementing other existing reviews that focus on mathematical models or broad AI-based simulations, the present review focuses on the use of AI-based fouling prediction models, namely, artificial neural networks (ANNs) and their derivatives, to provide deeper insights into the strengths, weaknesses, potential, and areas of improvement associated with such models for membrane fouling prediction.

Investigating the Path Tracking Algorithm Based on BP Neural Network

In this paper, we propose an adaptive path tracking algorithm based on the BP (back propagation) neural network to increase the performance of vehicle path tracking in different paths. Specifically, based on the kinematic model of the vehicle, the front wheel steering angle of the vehicle was derived with the PP (Pure Pursuit) algorithm, and related parameters affecting path tracking accuracy were analyzed. In the next step, BP neural networks were introduced and vehicle speed, radius of path curvature, and lateral error were used as inputs to train models. The output of the model was used as the control coefficient of the PP algorithm to improve the accuracy of the calculation of the front wheel steering angle, which is referred to as the BP-PP algorithm in this paper. As a final step, simulation experiments and real vehicle experiments are performed to verify the algorithm's performance. Simulation experiments show that compared with the traditional path tracking algorithm, the average tracking error of BP-PP algorithm is reduced by 0.025 m when traveling at a speed of 3 m/s on a straight path, and the average tracking error is reduced by 0.27 m, 0.42 m, and 0.67 m, respectively, at a speed of 1.5 m/s with a curvature radius of 6.8 m, 5.5 m, and 4.5 m, respectively. In the real vehicle experiment, an electric patrol vehicle with an autonomous tracking function was used as the experimental platform. The average tracking error was reduced by 0.1 m and 0.086 m on a rectangular road and a large curvature road, respectively. Experimental results show that the proposed algorithm performs well in both simulation and actual scenarios, improves the accuracy of path tracking, and enhances the robustness of the system. Moreover, facing paths with changes in road curvature, the BP-PP algorithm achieved significant improvement and demonstrated great robustness. In conclusion, the proposed BP-PP algorithm reduced the interference of nonlinear factors on the system and did not require complex calculations. Furthermore, the proposed algorithm has been applied to the autonomous driving patrol vehicle in the park and achieved good results.

Spatial distribution, contamination characteristics and ecological-health risk assessment of toxic heavy metals in soils near a smelting area

Soil heavy metals (HMs) contamination stemming from smelting and mining activities is becoming a global concern due to its devastating impacts on the environment and human health. In this study, 128 soil samples were investigated to assess the spatial distribution, contamination characteristics, ecological and human health risk of HMs in soils near a smelting area by using BP artificial neural network (BP-ANN) and Monte Carlo simulation. The results showed that the concentrations of all five HMs in the soil greatly exceeded the background value of study area with a basic trend: Pb > As > Cr > Cd > Hg, indicating a high pollution level. Arsenic and lead were the major pollutants in the study area with an exceedance rate of 78.95% and 28.95%, respectively. The toxic fume and dust emitted during the smelting process were identified as the major sources of HMs pollution in soil, while Cd pollution was mainly caused by agricultural activities near the study area. The probabilistic risk assessment suggested that the average HQ values of five HMs for children and adults exceeded the acceptable threshold with a trend: As > Pb > Cr > Cd > Hg. The average CR values of As, Cr and Pb for all population were greatly larger than the acceptable threshold (CR ≥ 1), indicating a high cancer risk. However, the CR values of Cd for adults and children were within the acceptable threshold (CR < 1), implying no cancer risk. The results of the present study can provide some insight into the contamination characteristics, ecological and human health risk of HMs in contaminated soils by mining and smelting activities, which can help prevent and control soil pollution and environmental risk.

Appraisal of machine learning techniques for predicting emerging disinfection byproducts in small water distribution networks

Monitoring emerging disinfection byproducts (DBPs) is challenging for many small water distribution networks (SWDNs), and machine learning-based predictive modeling could be an alternative solution. In this study, eleven machine learning techniques, including three multivariate linear regression-based, three regression tree-based, three neural networks-based, and two advanced non-parametric regression techniques, are used to develop models for predicting three emerging DBPs (dichloroacetonitrile, chloropicrin, and trichloropropanone) in SWDNs. Predictors of the models include commonly-measured water quality parameters and two conventional DBP groups. Sampling data of 141 cases were collected from eleven SWDNs in Canada, in which 70 % were randomly selected for model training and the rest were used for validation. The modeling process was reiterated 1000 times for each model. The results show that models developed using advanced regression techniques, including support vector regression and Gaussian process regression, exhibited the best prediction performance. Support vector regression models showed the highest prediction accuracy (R² =0.94) and stability for predicting dichloroacetonitrile and trichloropropanone, and Gaussian process regression models are optimal for predicting chloropicrin (R² =0.92). The difference is likely due to the much lower concentrations of chloropicrin than dichloroacetonitrile and trichloropropanone. Advanced non-parametric regression techniques, characterized by a probabilistic nature, were identified as most suitable for developing the predictive models, followed by neural network-based (e.g., generalized regression neural network), regression tree-based (e.g., random forest), and multivariate linear regression-based techniques. This study identifies promising machine learning techniques among many commonly-used alternatives for monitoring emerging DBPs in SWDNs under data constraints.

A BPNN-based ecologically extended input-output model for virtual water metabolism network management of Kazakhstan

In this study, a back-propagation-neural-network-based ecologically extended input-output model (abbreviated as BPNN-EIOM) is developed for virtual water metabolism network (VWMN) management. BPNN-EIOM can identify key consumption sectors, simulate performance of VWMN, and predict water consumption. BPNN-EIOM is then applied to analyzing VWMN of Kazakhstan, where multiple scenarios under different gross domestic production (GDP) growth rates, sectoral added values, and final demands are designed for determining the optimal management strategies. The major findings are (i) Kazakhstan typically relies on net virtual water import (reaching 1497.9 × 10⁶ m³ in 2015); (ii) agriculture is the major exporter and advanced manufacture is the major importer; (iii) by 2025, Kazakhstan's water consumption would increase to [19322, 22016] × 10⁶ m³ under multiple scenarios; (iv) when Kazakhstan's GDP growth rate, manufacturing's added value, and final demand are scheduled to 5.5%, 8.5%, and 5.8%, its VWMN can reach the optimum. The findings are useful for decision makers to optimize Kazakhstan's industrial structure, mitigate the national water scarcity, and promote its socio-economic sustainable development.

Recent advances in shelf life prediction models for monitoring food quality

Each year, 1.3 billion tons of food is lost due to spoilage or loss in the supply chain, accounting for approximately one third of global food production. This requires a manufacturer to provide accurate information on the shelf life of the food in each stage. Various models for monitoring food quality have been developed and applied to predict food shelf life. This review classified shelf life models and detailed the application background and characteristics of commonly used models to better understand the different uses and aspects of the commonly used models. In particular, the structural framework, application mechanisms, and numerical relationships of commonly used models were elaborated. In addition, the study focused on the application of commonly used models in the food field. Besides predicting the freshness index and remaining shelf life of food, the study addressed aspects such as food classification (maturity and damage) and content prediction. Finally, further promotion of shelf life models in the food field, use of multivariate analysis methods, and development of new models were foreseen. More reliable transportation, processing, and packaging methods could be screened out based on real-time food quality monitoring.

Predictive modelling and optimization of an airlift bioreactor for selenite removal from wastewater using artificial neural networks and particle swarm optimization

Selenite (Se⁴⁺) is the most toxic of all the oxyanion forms of selenium. In this study, a feed forward back propagation (BP) based artificial neural network (ANN) model was developed for a fungal pelleted airlift bioreactor (ALR) system treating selenite-laden wastewater. The performance of the bioreactor, i.e., selenite removal efficiency (RE_selenite) (%) was predicted through two input parameters, namely, the influent selenite concentration (IC_selenite) (10 mg/L - 60 mg/L) and hydraulic retention time (HRT) (24 h - 72 h). After training and testing with 96 sets of data points using the Levenberg-Marquardt algorithm, a multi-layer perceptron model (2-10-1) was established. High values of the correlation coefficient (0.96 ≤ R ≤ 0.98), along with low root mean square error (1.72 ≤ RMSE ≤ 2.81) and mean absolute percentage error (1.67 ≤ MAPE ≤ 2.67), clearly demonstrate the accuracy of the ANN model (> 96%) when compared to the experimental data. To ensure an efficient and economically feasible operation of the ALR, the process parameters were optimized using the particle swarm optimization (PSO) algorithm coupled with the neural model. The RE_selenite was maximized while minimizing the HRT for a preferably higher range of IC_selenite. Thus, the most favourable optimum conditions were suggested as: IC_selenite - 50.45 mg/L and HRT - 24 h, resulting in RE_selenite of 69.4%. Overall, it can be inferred that ANN models can successfully substitute knowledge-based models to predict the RE_selenite in an ALR, and the process parameters can be effectively optimized using PSO.

Quantification of interfacial interaction related with adhesive membrane fouling by genetic algorithm back propagation (GABP) neural network

Since adhesive membrane fouling is critically determined by the interfacial interaction between a foulant and a rough membrane surface, efficient quantification of the interfacial interaction is critically important for adhesive membrane fouling mitigation. As a current available method, the advanced extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory involves complicated rigorous thermodynamic equations and massive amounts of computation, restricting its application. To solve this problem, artificial intelligence (AI) visualization technology was used to analyze the existing literature, and the genetic algorithm back propagation (GABP) artificial neural network (ANN) was employed to simplify thermodynamic calculation. The results showed that GABP ANN with 5 neurons could obtain reliable prediction performance in seconds, versus several hours or even days time-consuming by the advanced XDLVO theory. Moreover, the regression coefficient (R) of GABP reached 0.9999, and the error between the prediction results and the simulation results was less than 0.01%, indicating feasibility of the GABP ANN technique for quantification of interfacial interaction related with adhesive membrane fouling. This work provided a novel strategy to efficiently optimize the thermodynamic prediction of adhesive membrane fouling, beneficial for better understanding and control of adhesive membrane fouling.

Prediction performance analysis of neural network models for an electrical discharge turning process

In many of the modern-day manufacturing industries, electrical discharge machining (EDM) now appears as an effective non-traditional material removal process for generating intricate shape geometries on various hard-to-cut work materials to meet the ever-increasing demands of higher dimensional accuracy and better surface quality. Development of an appropriate prediction model for any of the EDM processes is quite difficult due to complex material removal mechanism, and dynamic interactions between the input parameters and responses. To address the problem, this paper proposes development and deployment of five neural network models, i.e. feed forward neural network, convolutional neural network, recurrent neural network, general regression neural network and long short term memory-based recurrent neural network as effective prediction tools for an electrical discharge turning (EDT) process. The EDT is a variant of EDM process involving removal of material from cylindrical workpieces. The input parameters for the considered EDT process are magnetic field, pulse current, pulse duration and angular velocity, whereas, the responses are material removal rate and overcut. Several statistical error metrics, like R-squared (R²), adjusted R-squared (R²_adj), root mean square error and relative root mean square error are employed to compare the prediction accuracy of all the investigated neural network models. Based on a past experimental dataset, it is observed that long short term memory-based recurrent neural network provides more accurate prediction of both the responses under consideration. On the other hand, general regression neural network is noticed to be extremely robust having highly repetitive prediction performance.

Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model

Demand forecasting is a scientific and methodical assessment of future demand for a critical product.The effective Demand Forecast Model (DFM) enables pharmaceutical companies to be successful in the global market. The purpose of this research paper is to validate various shallow and deep neural network methods for demand forecasting, with the aim of recommending sales and marketing strategies based on the trend/seasonal effects of eight different groups of pharmaceutical products with different characteristics. The root mean squared error (RMSE) is used as the predictive accuracy of DFMs. This study also found that the mean RMSE value of the shallow neural network-based DFMs was 6.27 for all drug categories, which was lower than deep neural network models. According to the findings, DFMs based on shallow neural networks can effectively estimate future demand for pharmaceutical products.

An approach to removing COD and BOD based on polycarbonate mixed matrix membranes that contain hydrous manganese oxide and silver nanoparticles: A novel application of artificial neural network based simulation in MATLAB

This study aimed to determine the efficacy of novel ultrafiltration and mixed matrix membrane (MMM) composed of hydrous manganese oxide (HMO) and silver nanoparticles (Ag-NPs) for the removal of biological oxygen demand (BOD) and chemical oxygen demand (COD). In the polycarbonate (PC) MMM, the weight percent of HMO and Ag-NP has been increased from 5% to 10%. A neural network (ANN) was used in this study to compare PC-HMO and Ag-NP. MMM was evaluated in combination with HMO and Ag-NP loadings in order to assess their effects on pure water flux, mean pore size, porosity, and efficacy in removing BOD and COD. HMO and Ag-NPs can decrease membrane porosity in the casting solution while increasing mean pore size. According to the study's findings, the artificial neural network model appears to be highly appropriate for predicting the removal of BOD and COD. To develop a successful model, a suitable input dataset was selected, which consisted of BOD and COD. An ideal model architecture for MMM was proposed based on an optimal number of hidden layers (2 layers) and neurons (5-8 neurons). Experiments and predicted data show a strong correlation between the developed models. BOD was predicted with an excellent R2 and a low root mean square error (RMSE) of 0.99 and 0.05%, respectively, while COD was predicted with an excellent R2 and a low RMSE of 0.99 and 0.09%, respectively. Based on the results, Ag-NP was found to be an excellent candidate for the preparation of MMMs as well as convenient for the removal of BOD and COD from polluted water sources.

Impacts of Calcium Addition on Humic Acid Fouling and the Related Mechanism in Ultrafiltration Process for Water Treatment

Humic acid (HA) is a major natural organic pollutant widely coexisting with calcium ions (Ca²⁺) in natural water and wastewater bodies, and the coagulation-ultrafiltration process is the most typical solution for surface water treatment. However, little is known about the influences of Ca²⁺ on HA fouling in the ultrafiltration process. This study explored the roles of Ca²⁺ addition in HA fouling and the potential of Ca²⁺ addition for fouling mitigation in the coagulation-ultrafiltration process. It was found that the filtration flux of HA solution rose when Ca²⁺ concentration increased from 0 to 5.0 mM, corresponding to the reduction of the hydraulic filtration resistance. However, the proportion and contribution of each resistance component in the total hydraulic filtration resistance have different variation trends with Ca²⁺ concentration. An increase in Ca²⁺ addition (0 to 5.0 mM) weakened the role of internal blocking resistance (9.02% to 4.81%) and concentration polarization resistance (50.73% to 32.17%) in the total hydraulic resistance but enhanced membrane surface deposit resistance (33.93% to 44.32%). A series of characterizations and thermodynamic analyses consistently suggest that the enlarged particle size caused by the Ca²⁺ bridging effect was the main reason for the decreased filtration resistance of the HA solution. This work revealed the impacts of Ca²⁺ on HA fouling and demonstrated the feasibility to mitigate fouling by adding Ca²⁺ in the ultrafiltration process to treat HA pollutants.

Anti-Pectin Fouling Performance of Dopamine and (3-Aminopropy) Triethoxysilane-Coated PVDF Ultrafiltration Membrane

Due to the diversity and complexity of the components in traditional Chinese medicine (TCM) extracts, serious membrane fouling has become an obstacle that limits the application of membrane technology in TCM. Pectin, a heteropolysaccharide widely existing in plant cells, is the main membrane-fouling substance in TCM extracts. In this study, a hydrophilic hybrid coating was constructed on the surface of a polyvinylidene fluoride (PVDF) ultrafiltration (UF) membrane co-deposited with polydopamine (pDA) and (3-Aminopropy) triethoxysilane (KH550) for pectin antifouling. Characterization analysis showed that hydrophilic coating containing hydrophilic groups (–NH₃, Si-OH, Si-O-Si) formed on the surface of the modified membrane. Membrane filtration experiments showed that, compared with a matched group (FRR: 28.66%, Rr: 26.87%), both the flux recovery rate (FRR) and reversible pollution rate (Rr) of the pDA and KH550 coated membrane (FRR: 48.07%, Rr: 44.46%) increased, indicating that pectin absorbed on the surface of membranes was more easily removed. Based on the extended Derjaguin–Laudau–Verwey–Overbeek (XDLVO) theory, the fouling mechanism of a PVDF UF membrane caused by pectin was analyzed. It was found that, compared with the pristine membrane (144.21 kT), there was a stronger repulsive energy barrier (3572.58 kT) to confront the mutual adsorption between the coated membrane and pectin molecule. The total interface between the modified membrane and the pectin molecule was significantly greater than the pristine membrane. Therefore, as the repulsion between them was enhanced, pectin molecules were not easily adsorbed on the surface of the coated membrane.

Optimization of Sour Water Stripping Unit Using Artificial Neural Network–Particle Swarm Optimization Algorithm

Sour water stripping can treat the sour water produced by crude oil processing, which has the effect of environmental protection, energy saving and emission reduction. This paper aims to reduce energy consumption of the unit by strengthening process parameter optimization. Firstly, the basic model is established by utilizing Aspen Plus, and the optimal model is determined by comparative analysis of back propagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) models. Then, the sensitivity analysis of Sobol is used to select the operating variables that have a significant influence on the energy consumption of the sour water stripping system. Finally, the particle swarm optimization (PSO) algorithm is used to optimize the operating conditions of the sour water stripping unit. The results show that the RBFNN model is more accurate than other models. Its network structure is 5-66-1, and the expected value has an approximately linear relationship with the output value. Through sensitivity analysis, it is found that each operating parameter has an impact on the sour water stripping process, which needs to be optimized by the PSO algorithm. After 210 iterations of the PSO algorithm, the optimal system energy consumption is obtained. In addition, the cold/hot feed ratio, sideline production position, tower bottom pressure, hot feed temperature, and cold feed temperature are 0.117, 18, 436 kPa, 146 °C, and 35 °C, respectively; the system energy consumption is 5.918 MW. Compared with value of 7.128 MW before optimization, the energy consumption of the system is greatly reduced by 16.97%, which shows that the energy-saving effect is very significant.

Leakage Current Sensor and Neural Network for MOA Monitoring

Metal-oxide arrester (MOA) has been widely used in electric power systems. The leakage current monitoring of MOA can not only detect the MOA's running state continuously and intelligently but also reduce the unexpected outage of the equipment, which is also beneficial to the stability of the grid. The MOA loses its protection function due to various faults caused by excessive leakage current in actual running. This article studies the monitoring method of MOA based on leakage current sensor and back propagation (BP) neural network. At first, we design a novel leakage current sensor to acquire the leakage current of MOA. Then, the leakage current measurement of MOA based on harmonic analysis is proposed. Finally, the strong training ability of the BP neural network is used to train some key parameters that can reflect the aging of MOA so as to monitor the MOA state. The experimental results show that the leakage current acquired from the simulation is close to the actual leakage current that needs to be measured. It is also shown that the proposed method has good anti-interference and can effectively monitor the aging of MOA. Through the training of the BP neural network, the experiments prove that the training method in this article is superior to other neural network training methods obviously.

Identification of environmental microorganism using optimally fine-tuned convolutional neural network

To not only optimize the hyper-parameters of the classification layer of dense convolutional network with 201 convolutional layers (DenseNet-201) but also use data augmentation processes could enhance the performance of DenseNet-201, and DenseNet-201 is rarely applied to the identifications of the environmental microorganism (EM) images. Hence, this study was to propose the optimally fine-tuned DenseNet-201 (OFTD) with data augmentation to better classify the EM images on Environmental Microorganism Dataset (EMDS). The training dataset was composed of 70% Environmental Microorganism Dataset (EMDS) images and so was mainly used to fit the parameters of convolutional layers of optimally fine-tuned DenseNet-201 (OFTD). Meanwhile, the other EMDS images were considered as the testing dataset and used to qualify the performance of the OFTD. Also, gradient-weighted class activation mapping method (Grad-CAM) was adopted to visually illustrate the dominant features of the EM images. Based on the results, the OFTD model with data augmentation achieved the highest classification accuracy of 98.4%. In this case, so its stability and accuracy were guaranteed. Besides, the optimally fine-tuned classification layer is considered a more efficient method than the data augmentation technique adopted in this study when it comes to the improvement of the performance in DenseNet-201 implemented on EMDS. Grad-CAM highlighted the coarse EM features identified effectively by the OFTD; for example, foot and stalk were considered as the dominated features of Rotifera and Vorticella, respectively. In summary, the proposed OFTD with data augmentation could provide an efficient solution for the EM detection in digital microscope.

A unified thermodynamic fouling mechanism based on forward osmosis membrane unique properties: An asymmetric structure and reverse solute diffusion

Fouling mechanism of the forward osmosis membrane, which was peculiarly featured by the asymmetric membrane structure and reverse solute diffusion, was investigated at the molecular level and from the energy perspective. Two noteworthy fouling behaviors were observed in batch fouling tests conducted in AL-FS mode (active layer facing feed solution) and AL-DS mode (active layer facing draw solution) after filtering foulants with identical volume: 1) after filtering 100 mL of foulants, the flux decline rate in AL-DS mode was 1.78 times faster than that in AL-FS mode, but the flux decline behaviors of the two modes were similar in the subsequent filtration stages; 2) although the foulant layer weight of the same mode increased linearly in middle and late stages, the flux loss rate was distinctly different. Thermodynamic analysis indicated that the attractive interaction energy between the foulants and the support layer was about 5 times higher than that between the foulants and the active layer, well interpreting the higher flux decline rate of AL-DS mode in initial stage. Meanwhile, a non-invasive microscope observed that the structure of the fouling layer remarkably changed from loose to dense in the middle stage, and stabilized in the late stage. Furthermore, quantum chemistry calculation proved that the reverse diffusion of NaCl brought alginate molecular chains closer, whereas the distance between them tended to be constant as the continuous increase of NaCl. Based on these findings, the thermodynamic fouling mechanism proposed by combining the structure change process of the fouling layer with Flory-Huggins lattice theory satisfactorily interpreted the noteworthy fouling behaviors caused by reverse NaCl diffusion in middle and late stages. The revealed fouling mechanism unifies the adhesion and filtration behaviors related to the unique properties of FO membrane, deepening understanding of membrane fouling in the dynamic and complex ternary system of the FO process.

Membrane fouling in a microalgal-bacterial membrane photobioreactor: Effects of P-availability controlled by N:P ratio

Microalgal-bacterial membrane photobioreactor (MB-MPBR) is a promising technology to simultaneously remove organics and nutrients from wastewater. However, membrane fouling in MB-MPBR was seldom studied. In this study, potential effects of P-availability on biomass properties and membrane fouling in MB-MPBR were investigated. Under a nitrogen sufficient condition, a lower N:P ratio of 3.9:1 (P-rich) caused more severe membrane fouling. The dominant fouling mechanism was cake layer formation. Serial characterization showed a smaller particle size distribution (PSD), more free microalgae and significantly different surface composition of microalgal-bacterial flocs at N:P ratio of 3.9:1 compared with that of 9.7:1. The variations on PSD and surface composition were fully consistent with that of filtration resistance and thus considered as the primary contributors to the different fouling performance. The above results suggested that controlling microalgae/bacteria consortium in a good ratio by optimizing operating conditions is the key event for membrane fouling control in MB-MPBRs.

Temperature and immigration effects on quorum sensing in the biofilms of anaerobic membrane bioreactors

Quorum sensing (QS), a microbial communication mechanism modulated by acyl homoserine lactone (AHL) molecules impacts biofilm formation in bioreactors. This study investigated the effects of temperature and immigration on AHL levels and biofouling in anaerobic membrane bioreactors. The hypothesis was that the immigrant microbial community would increase the AHL-mediated QS, thus stimulating biofouling and that low temperatures would exacerbate this. We observed that presence of immigrants, especially when exposed to low temperatures indeed increased AHL concentrations and fouling in the biofilms on the membranes. At low temperature, the concentrations of the main AHLs observed, N-dodecanoyl-L-homoserine lactone and N-decanoyl-L-homoserine lactone, were significantly higher in the biofilms than in the sludge and correlated significantly with the abundance of immigrant bacteria. Apparently low temperature, immigration and denser community structure in the biofilm stressed the communities, triggering AHL production and excretion. These insights into the social behaviour of reactor communities responding to low temperature and influx of immigrants have implications for biofouling control in bioreactors.

Adaptive PID Control and Its Application Based on a Double-Layer BP Neural Network

In this paper, focusing on the inconvenience of variable value PID based on manual parameter adjustment for the hydraulic drive unit (HDU) of a legged robot, a method employing double-layer back propagation (BP) neural networks for learning the law of PID control parameters is proposed. The first layer is used to learn the relationship between different control parameters and the control performance of the system under various working conditions. The second layer is used to study the relationship between the parameters of the working conditions and the optimizing control parameters under various working conditions. The effectiveness of the proposed control method was verified by simulation and experiment. The results showed that the proposed method can provide a theoretical and experimental basis for the selection of control parameters, and can be extended to similar controllers, therefore possessing engineering application value.

New methods based on back propagation (BP) and radial basis function (RBF) artificial neural networks (ANNs) for predicting the occurrence of haloketones in tap water

Haloketones (HKs) is one class of disinfection by-products (DBPs) which is genetically toxic and mutagenic. Monitoring HKs in drinking water is important for drinking water safety, yet it is a time-consuming and laborious job. Developing predictive models of HKs to estimate their occurrence in drinking water is a good alternative, but to date no study was available for HKs modeling. This study was to explore the feasibility of linear, log linear regression models, back propagation (BP) as well as radial basis function (RBF) artificial neural networks (ANNs) for predicting HKs occurrence (including dichloropropanone, trichloropropanone and total HKs) in real water supply systems. Results showed that the overall prediction ability of RBF and BP ANNs was better than linear/log linear models. Though the BP ANN showed excellent prediction performance in internal validation (N₂₅ = 98-100%, R² = 0.99-1.00), it could not well predict HKs occurrence in external validation (N₂₅ = 62-69%, R² = 0.202-0.848). Prediction ability of RBF ANN in external validation (N₂₅ = 85%, R² = 0.692-0.909) was quite good, which was comparable to that in internal validation (N₂₅ = 74-88%, R² = 0.799-0.870). These results demonstrated RBF ANN could well recognized the complex nonlinear relationship between HKs occurrence and the related water quality, and paved a new way for HKs prediction and monitoring in practice.

Synergistic fouling behaviors and mechanisms of calcium ions and polyaluminum chloride associated with alginate solution in coagulation-ultrafiltration (UF) process

Effects of calcium ions and polyaluminum chloride (PACl) on membrane fouling in coagulation-ultrafiltration (UF) process were investigated in this study. Filtration tests demonstrated three interesting filtration behaviors: 1) high specific filtration resistance (SFR) of alginate solution with low CaCl₂ or PACl addition (e. g. 3.51×10¹⁵ m·kg ^-1 under the condition of 1.5 mM CaCl₂ addition); 2) unimodal pattern of alginate SFR with PACl or CaCl₂ addition alone; 3) synergistic effects between CaCl₂ and PACl on alginate SFR. It was found that, the foulant morphological changes driven by the thermodynamic mechanisms based on Flory-Huggins lattice theory take the critical roles in these filtration behaviors. Density functional theory (DFT) calculations showed that initial coordination of Ca²⁺ and Al³⁺ ions with alginates tended to form tetrahedron geometry and geometry of coordinating three terminal carboxyl groups, respectively, which facilitated to elongate the alginate chains (without clustering the flocs) and form more stable gel, increasing SFR. Improving Ca²⁺ and Al³⁺ dosages triggered transition to other geometries for clustering polymeric network and flocculation, reducing SFR. Due to the higher binding affinity of Ca²⁺ over Al³⁺, Ca²⁺ and Al³⁺ sequentially take roles of enlarging polymeric network and clustering the coordination compounds, and then facilitate to form large size flocs and reduce SFR, causing the synergistic effects between CaCl₂ and PACl additions. The proposed thermodynamic mechanisms satisfactorily explained these interesting fouling behaviors, allowing to further optimize coagulation-UF process.

Green preparation of polyvinylidene fluoride loose nanofiltration hollow fiber membranes with multilayer structure for treating textile wastewater

In this work, polyvinylidene fluoride (PVDF) loose nanofiltration (NF) hollow fiber membranes with multilayer structure were prepared successfully based on a solvent-free process. Graphene oxide (GO) was used to cover the interface pores of the pristine PVDF membranes via vacuum filtration, and polypyrrole (PPy) was polymerized on the surface to further decorate the membrane structure. Interestingly, the modified membranes exhibited a multilayer structure due to synergistic effect of GO and PPy. The structure and property of PVDF loose NF membranes were investigated in detail. After modifying by GO and PPy, the hydrophilicity improved obviously. Moreover, the molecular weight cut off (MWCO) was about 3580 Da, and the smallest pore size of skin layer decreased to 2.5-4 nm. Furthermore, the PVDF loose NF hollow fiber membranes presented a high dye rejection (˃98.5%) for negative dyes, whereas a low salt rejection for NaCl (about 4%), showing a great potential for separating dye/salt accurately. Specifically, there were not any solvent used in all the preparation processes. The work offered a novel strategy for green preparation of loose NF membranes.

Study on the binding mechanism of thiamethoxam with three model proteins:spectroscopic studies and theoretical simulations

As a top-selling neonicotinoid insecticide widely used in the field, thiamethoxam is an environmental pollutant because of the accumulation in ecosystem and has also been reported that it has potential risks to the health of mammals even humans. In order to understand the binding mechanism of thiamethoxam with biological receptors, spectroscopic techniques and theoretical simulations was used to explore the specific interactions between thiamethoxam and proteins. Interestingly, the results indicated that hydrophobic interaction as the main driving force, thiamethoxam formed a single binding site complex with proteins spontaneously, resulting in a decrease in the esterase-like activity of human serum albumin. The results of computer simulation showed that there were hydrophobic, electrostatic and hydrogen bonding interactions between thiamethoxam and receptors. The results of experiment and computer simulation were mutually confirmed, so a model was established for the interaction between the two which uncovered the structural characteristics of the binding site. This research provided new insights for the structure optimization of thiamethoxam, as well as gave an effective reference for evaluating the risk of thiamethoxam systemically in the future.

Lip balm drying promotes virus attachment: Characterization of lip balm coatings and XDLVO modeling

HYPOTHESIS

Drying-induced decrease in lip balm surface energy enhances virus adhesion due to the emergence of strong hydrophobic colloid-surface interactions.

EXPERIMENTS

A protocol was developed for preparing lip balm coatings to enable physicochemical characterization and adhesion studies. Surface charge and hydrophobicity of four brands of lip balm (dry and hydrated) and human adenovirus 5 (HAdV5) were measured and used to calculate the extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) energy of interactions between lip balm coatings and HAdV5 as well as four other colloids: HAdV40, MS2 and P22 bacteriophages, and SiO2. Quartz crystal microbalance with dissipation monitoring (QCM-D) tests employed SiO2 colloids, HAdV5 and hydrated lip balms.

FINDINGS

Drying of lip balms results in a dramatic decrease of their surface energy (δΔGsws≥ 83.0 mJ/m2) making the surfaces highly hydrophobic. For dry lip balms, the interaction of the balm surface with all five colloids is attractive. For lip balms hydrated in 150 mM NaCl (ionic strength of human saliva), XDLVO calculations predict that hydrophilic colloids (MS2, P22, SiO2) may attach into shallow secondary minima. Due to the relative hydrophobicity of human adenoviruses, primary maxima in XDLVO profiles are low or non-existent making irreversible deposition into primary energy minima possible. Preliminary QCM-D tests with SiO2 colloids and HAdV5 confirm deposition on hydrated lip balms.

Prepared PANI@nano hollow carbon sphere adsorbents with lappaceum shell like structure for high efficiency removal of hexavalent chromium

Herein, the novel polyaniline@nano hollow carbon sphere (PANI@NHCS) adsorbents with different mass of NHCS were prepared by in-situ polymerization method. The microstructure of obtained PANI@NHCS-10, PANI@NHCS-20, PANI@NHCS-30 and PANI@NHCS-40 samples were observed through both scanning electron microscopy (SEM) and transmission electron microscopy (TEM), which showed that the PANI@NHCS-30 possessed hollow structure like lappaceum shell. Then, the performance of obtained PANI@NHCS-30 was studied for removing hexavalent chromium (Cr(VI)) from waste water. With the help of unique hollow structure and reduction ability of PANI@NHCS-30, the Cr(VI) was fleetly adsorbed and then reduced to less toxic Cr(III). The maximum adsorption capacity was 250.0 mg/g for PANI@NHCS-30 under the optimal condition. Moreover, the effects of initial Cr(VI) concentration, solution pH and different ions on the adsorption performance were investigated in detail. Importantly, the PANI@NHCS-30 still shows superb adsorption ability after five cycles, which suggests its satisfactory reusability ability. The accumulated data revealed the crucial role of PANI and hollow structure co-promoting effect on Cr(VI) reduction reactions over PANI@NHCS-30, which could be applied to the practical use.

Establishment of the Predicting Models of the Dyeing Effect in Supercritical Carbon Dioxide Based on the Generalized Regression Neural Network and Back Propagation Neural Network

With the growing demand of supercritical carbon dioxide (SC-CO2) dyeing, it is important to precisely predict the dyeing effect of supercritical carbon dioxide. In this work, Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) models have been employed to predict the dyeing effect of SC-CO2. These two models have been constructed based on published experimental data and calculated values. A total of 386 experimental data sets were used in the present work. In GRNN and BPNN models, two input parameters, such as temperature, pressure, dye stuff types, carrier types and dyeing time, were selected for the input layer and one variable, K/S value or dye-uptake, was used in the output layer. It was found that the values of mean-relative-error (MRE) for BPNN model and for GRNN model are 3.27–6.54% and 1.68–3.32%, respectively. The results demonstrate that both BPNN and GPNN models can accurately predict the effect of supercritical dyeing but the former is better than the latter.

Different fouling propensities of loosely and tightly bound extracellular polymeric substances (EPSs) and the related fouling mechanisms in a membrane bioreactor

In this study, fouling propensities of loosely bound extracellular polymeric substances (LB-EPSs) and tightly bound EPSs (TB-EPSs) in a membrane bioreactor (MBR) were investigated. It was found that, both the LB-EPSs and TB-EPSs possessed rather high specific filtration resistance (SFR), and LB-EPSs possessed about three times higher SFR but a lower adhesion ability than the TB-EPSs. A series of characterizations demonstrated that LB-EPSs had higher ratio of proteins to polysaccharides (PN/PS ratio), lower CO bonds content, higher hydrophilicity, higher deformation or mixing ability and more abundant high molecular weight (MW) substances than TB-EPSs. Thermodynamic analyzes revealed that the total interaction energy between the TB-EPSs and membrane was always attractive and strengthened, well explaining the higher adhesion ability of the TB-EPSs than the LB-EPSs. Meanwhile, the filtration process was found to be associated with gel layer formation, and the high SFR of EPSs was caused by the chemical potential change in gel layer filtration. According to the Flory-Huggins lattice theory, LB-EPSs tended to form a gel layer with higher cross-linking and/or polymer entanglement level because they contained more abundant high molecular weight (MW) substance, corresponding to higher SFR than that of the TB-EPSs. The proposed thermodynamic mechanisms well interpreted the different fouling propensities of LB-EPSs and TB-EPSs in MBRs.

Membrane fouling caused by biological foams in a submerged membrane bioreactor: Mechanism insights

Though sludge foaming often occurs and thus causes serious membrane fouling in membrane bioreactors (MBRs), the fouling mechanisms related with the foaming phenomenon have not been well addressed, hindering better understanding and solving foaming problem. In this work, it was interestingly found that, the foulants during the foaming period possessed extremely high specific filtration resistance (SFR) (over 10¹⁶ m kg^-1) and strong adhesion ability to membrane surface. Chemical characterization showed that the proteins (178.57 mg/L) and polysaccharides (209.21 mg/L) in the foaming sample were about 6.4 times and 5.4 times of those in the supernatant sample, suggesting existence of a mechanism permitting continuous production of these foulants in the MBR during the foaming period. It was revealed that the fouling caused by foams was associated with gel layer filtration process, and the extremely high SFR can be interpreted by chemical potential change in the gel filtration process depicted in Flory-Huggins theory. Meanwhile, analyses by the extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory showed that the strong adhesion ability stemmed from the high interaction energy between the foaming foulants and membrane surface. In addition, 16S rDNA gene sequencing identified that the abundance of the foaming related bacteria species in the sludge suspension during the foaming period was more than 10 times of that during the non-foaming period. This study offered new mechanism insights into foaming fouling in MBRs.

Modeling water flux in osmotic membrane bioreactor by adaptive network-based fuzzy inference system and artificial neural network

Osmotic Membrane Bioreactor (OMBR) is an emerging technology for wastewater treatment with membrane fouling as a major challenge. This study aims to develop Adaptive Network-based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models in simulating and predicting water flux in OMBR. Mixed liquor suspended solid (MLSS), electrical conductivity (EC) and dissolved oxygen (DO) were used as model inputs. Good prediction was demonstrated by both ANFIS models with R² of 0.9755 and 0.9861, and ANN models with R² of 0.9404 and 0.9817, for thin film composite (TFC) and cellulose triacetate (CTA) membranes, respectively. The root mean square error for TFC (0.2527) and CTA (0.1230) in ANFIS models was lower than in ANN models at 0.4049 and 0.1449. Sensitivity analysis showed that EC was the most important factor for both TFC and CTA membranes in ANN models, while EC (TFC) and MLSS (CTA) are key parameters in ANFIS models.

Inkjet printing assisted fabrication of polyphenol-based coating membranes for oil/water separation

While polyphenol-based coating has been regarded as a promising alternative to functionalize membrane surface, it usually suffers from problems of low-efficient procedure and low utilization rate of the polyphenolic compounds, hindering its large-scale implementations. To solve these problems, this study provided a first report on inkjet printing of polyphenols (catechol (CA) or tannic acid (TA)) and sodium periodate (SP) on a polyvinylidene fluoride (PVDF) membrane to improve membrane performance. A series of analyses showed the efficient formation of homogenous films on the PVDF membrane surface and the improvement of hydrophilicity by the inkjet printing technique. The PVDF membranes decorated with the optimized polyphenolic coating exhibited a promising oil/water separation efficiency (higher than 99%) with a high average water permeation flux of 5.2 times higher than that of the pristine membrane. Meanwhile, the modified membranes illustrated a good stability under acidic conditions (pH = 2-7). The novel method proposed in this study is facile, cost-saving and environment-friendly. The advantages of the proposed method and the modified membranes demonstrated the great significance of the proposed method in practical applications.