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Use of immobilized enzymatic membrane reactors (EMR) is an effective strategy to enhance oligodextran production efficiency. Nevertheless, the reaction-separation coupling mechanism has not been fully understood. In this study, we developed a model to disentangle the mechanism and interaction behind the reaction and separation phenomena in the immobilized EMR to eventually evaluate process performance for optimal oligodextran production. The simulation results show that in the immobilized EMR applying substrate feeding mode and high operating pressure enhances convective and diffusive flows through membrane, leading to a high transport rate of product. The optimal substrate concentration and enzyme loading amount need to match the selectivity and permeability of the membrane, in order to ensure rapid removal of the target oligodextrans. Therefore, the membranes with high permselectivity are required for obtaining desirable production efficiency and product quality. Although immobilized EMR has higher reaction rate at the beginning because of higher local enzyme concentration in the reaction zone, the EMR with free enzymes could outperform the immobilized EMR by using higher enzyme concentration due to its larger reaction area as the reaction continues. Our study provides deep insights into the reaction-separation coupling mechanism and EMR performance enhancement.

This study aims to demonstrate the application of deep learning to quantitatively describe long-term full-scale data observed from wastewater treatment plants (WWTPs) from the perspectives of process modeling, process analysis, and forecasting modeling. Approximately, 750,000 measurements including the influent flow rate, air flow rate, temperature, ammonium, nitrate, dissolved oxygen, and nitrous oxide (N2O) collected for more than a year from the Avedøre WWTP located in Denmark are utilized to develop a deep neural network (DNN) through supervised learning for process modeling, and the optimal DNN (R2 > 0.90) is selected for further evaluation. For process analysis, global sensitivity analysis based on variance decomposition is considered to identify the key parameters contributing to high N2O emission characteristics. For N2O forecasting, the proposed DNN-based model is compared with long short-term memory (LSTM), showing that the LSTM-based forecasting model performs significantly better than the DNN-based model (R2 > 0.94 and the root-mean-squared error is reduced by 64%). The results account for the feasibility of data-driven methods based on deep learning for quantitatively describing and understanding the rather complex N2O dynamics in WWTPs. Research into hybrid modeling concepts integrating mechanistic models of WWTPs (e.g., ASMs) with deep learning would be suggested as a future direction for monitoring N2O emissions from WWTPs.

Producción Científica, Methylocystis hirsuta, a type II methanotroph, has been experimentally demonstrated to be able to efficiently synthesize polyhydroxyalkanoates (PHA) from biogas under nutrient-limited conditions. A mechanistic model capable of describing the relevant processes of M. hirsuta, which is currently not available, would therefore lay a solid foundation for future practical demonstration and optimization of the PHA synthesis technology using biogas. To this end, dedicated batch tests were designed and conducted to obtain experimental data for different mechanistic processes of M. hirsuta. Through utilizing the experimental data of well-designed batch tests and following a step-wise model calibration/validation protocol, the stoichiometrics and kinetics of M. hirsuta are reported for the first time, including the yields of growth and PHA synthesis on CH4 (0.14 ± 0.01 g COD g–1 COD and 0.25 ± 0.02 g COD g–1 COD), the CH4 and O2 affinity constants (5.1 ± 2.1 g COD m–3 and 4.1 ± 1.7 g O2 m–3), the maximum PHA consumption rate (0.019 ± 0.001 g COD g–1 COD d–1), and the maximum PHA synthesis rate on CH4 (0.39 ± 0.05 g COD g–1 COD d–1). Through applying the developed model, an optimal O2:CH4 molar ratio of 1.6 mol O2 mol–1 CH4 was found to maximize the PHA synthesis by M. hirsuta. Practically, the model and parameters obtained would not only benefit the design and operation of bioreactors performing PHA synthesis from biogas, but also enable specific research on selection for type II methanotrophs in diverse environments., Australian Research Council (ARC) through Future Fellowship (FT160100195), Junta de Castilla y León y EU-FEDER (CLU 2017-09) y (UIC 71), European Union’s Horizon 2020 research and innovation program. grant agreement no. 790231.