A dynamical model of an aeration plant for wastewater treatment using a phenomenological based semi-physical modeling methodology

Dynamic real-time forecasting technique for reclaimed water volumes in urban river environmental management

In recent years, the utilization of wastewater recycling as an alternative water source has gained significant traction in addressing urban water shortages. Accurate prediction of wastewater quantity is paramount for effective urban river water resource management. There is an urgent need to develop advanced forecasting technologies to further enhance the accuracy and efficiency of water quantity predictions. Decomposition ensemble models have shown excellent predictive capabilities but are challenged by boundary effects when decomposing the original data sequence. To address this, a rolling forecast decomposition ensemble scheme was developed. It involves using a machine learning (ML) model for prediction and progressively integrating prediction outcomes into the original sequence using complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Long short-term memory (LSTM) is then applied for sub-signal prediction and ensemble. The ML-CEEMDAN-LSTM model was introduced for wastewater quantity prediction, compared with non-decomposed ML models, CEEMDAN-based LSTM models, and ML-CEEMDAN-based LSTM models. Three ML algorithms-linear regression (LR), gradient boosting regression (GBR), and LSTM-were examined, using real-time prediction data and historical monitoring data, with historical data selected using the decision tree method. The study used daily water volumes data from two reclaimed water plants, CH and WQ, in Beijing. The results indicate that (1) ML models varied in their selection of real-time factors, with LR performing best among ML models during testing; (2) the ML-CEEMDAN-LSTM model consistently outperformed ML models; (3) the ML-CEEMDAN-LSTM hybrid model performed better than the CEEMDAN-LSTM model across different seasons. This study offers a reliable and accurate approach for reclaimed water volumes forecasting, critical for effective water environment management.

An influent responsive control strategy with machine learning: Q-learning based optimization method for a biological phosphorus removal system

Biological phosphorus removal (BPR) is an economical and sustainable processes for the removal of phosphorus (P) from wastewater, achieved by recirculating activated sludge through anaerobic and aerobic (An/Ae) processes. However, few studies have systematically analyzed the optimal hydraulic retention times (HRTs) in anaerobic and aerobic reactions, or whether these are the most appropriate control strategies. In this study, a novel optimization methodology using an improved Q-learning (QL) algorithm was developed, to optimize An/Ae HRTs in a BPR system. A framework for QL-based BPR control strategies was established and the improved Q function, Q^t+1(s_t,s_t+1)=Q^t(s_t,s_t+1)+k·[R(s_t,s_t+1)+γ·maxa^tQ^t(s_t,s_t+1)-Q^t(s_t,s_t+1)] was derived. Based on the improved Q function and the state transition matrices obtained under different HRT step-lengths, the optimum combinations of HRTs in An/Ae processes in any BPR system could be obtained, in terms of the ordered pair combinations of the <current state-transition state>. Model verification was performed by applying six different influent chemical oxygen demand (COD) concentrations, varying from 150 to 600 mg L^-1 and influent P concentrations, varying from 12 to 30 mg L^-1. Superior and stable effluent qualities were observed with the optimal control strategies. This indicates that the proposed novel QL-based BPR model performed properly and the derived Q functions successfully realized real-time modelling, with stable optimal control strategies under fluctuant influent loads during wastewater treatment processes.

Intelligent Control/Operational Strategies in WWTPs through an Integrated Q-Learning Algorithm with ASM2d-Guided Reward

The operation of a wastewater treatment plant (WWTP) is a typical complex control problem, with nonlinear dynamics and coupling effects among the variables, which renders the implementation of real-time optimal control an enormous challenge. In this study, a Q-learning algorithm with activated sludge model No. 2d-guided (ASM2d-guided) reward setting (an integrated ASM2d-QL algorithm) is proposed, and the widely applied anaerobic-anoxic-oxic (AAO) system is chosen as the research paradigm. The integrated ASM2d-QL algorithms equipped with a self-learning mechanism are derived for optimizing the control strategies (hydraulic retention time (HRT) and internal recycling ratio (IRR)) of the AAO system. To optimize the control strategies of the AAO system under varying influent loads, Q matrixes were built for both HRTs and IRR optimization through the pair of <max reward-action> based on the integrated ASM2d-QL algorithm. 8 days of actual influent qualities of a certain municipal AAO wastewater treatment plant in June were arbitrarily chosen as the influent concentrations for model verification. Good agreement between the values of the model simulations and experimental results indicated that this proposed integrated ASM2d-QL algorithm performed properly and successfully realized intelligent modeling and stable optimal control strategies under fluctuating influent loads during wastewater treatment.