Integrated mathematical model to simulate the performance of a membrane bioreactor

Process innovations and circular strategies for closing the water loop in a process industry

By implementing advanced wastewater treatment technologies coupled with digital tools, high-quality water is produced to be reused within the industry, enhancing process efficiency and closing loops. This paper investigates the impact of three innovation tools (process, circular and digital) in a Solvay chemical plant. Four technologies of the wastewater treatment plant "WAPEREUSE" were deployed, predicting their performance by process modelling and simulation in the PSM Tool. The environmental impact was assessed using Life Cycle Assessment and compared to the impact of the current industrial effluent discharge. The circularity level was assessed through three alternative closed-loop scenarios: (1) conventional treatment and discharge to sea (baseline), (2) conventional and advanced treatment by WAPEREUSE and discharge to sea, (3) conventional and advanced treatment by WAPEREUSE and industrial water reuse through cross-sectorial symbiotic network, where effluents are exchanged among the process industry, municipality and a water utility. Scenario 1 has the lowest pollutants' removal efficiency with environmental footprint of 0.93 mPt/m3. WAPEREUSE technologies decreased COD by 98.3%, TOC by 91.4% and nitrates by 94.5%. Scenario 2 had environmental footprint of 1.12 mPt/m3. The cross-sectorial symbiotic network on the industrial value chain resulted in higher industrial circularity and sustainability level, avoiding effluents discharge. Scenario 3 is selected as the best option with 0.72 mPt per m3, reducing the environmental footprint by 21% and 36% compared to Scenarios 1 and 2, respectively.

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

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

Prolonging the Life Span of Membrane in Submerged MBR by the Application of Different Anti-Biofouling Techniques

The membrane bioreactor (MBR) is an efficient technology for the treatment of municipal and industrial wastewater for the last two decades. It is a single stage process with smaller footprints and a higher removal efficiency of organic compounds compared with the conventional activated sludge process. However, the major drawback of the MBR is membrane biofouling which decreases the life span of the membrane and automatically increases the operational cost. This review is exploring different anti-biofouling techniques of the state-of-the-art, i.e., quorum quenching (QQ) and model-based approaches. The former is a relatively recent strategy used to mitigate biofouling. It disrupts the cell-to-cell communication of bacteria responsible for biofouling in the sludge. For example, the two strains of bacteria Rhodococcus sp. BH4 and Pseudomonas putida are very effective in the disruption of quorum sensing (QS). Thus, they are recognized as useful QQ bacteria. Furthermore, the model-based anti-fouling strategies are also very promising in preventing biofouling at very early stages of initialization. Nevertheless, biofouling is an extremely complex phenomenon and the influence of various parameters whether physical or biological on its development is not completely understood. Advancing digital technologies, combined with novel Big Data analytics and optimization techniques offer great opportunities for creating intelligent systems that can effectively address the challenges of MBR biofouling.

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

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

Challenges in Environmental Science/Engineering and fate and innovative treatment/remediation of emerging pollutants

Solid waste Management: There are two articles in this section. Shi et al. (2021) investigated the unbalanced status and multidimensional influences of municipal solid waste management in Africa. It was identified that economic growth, urbanization and geographical location are the most critical factors influencing the unbalanced statue of MSW management in Africa.