Contaminated mining soils can induce the heavy metal pollution of environment through the release of heavy metal leachate. Magnetic biochar synthesized by the alkaline coprecipitation method was used as a microelectrode in the three-dimensional electrokinetic (3D-EKR) remediation. The biodegradable chelates HIDS, MGDA and PASP enhance 3D-EKR removal heavy metals from contaminated soil. Soil and electrolyte characteristics variation, removal efficiency and distribution, and chemical fractions of heavy metal variations were studied in this work. Result indicated that MGDA, HIDS and PASP could promote electric current increasing, and the PASP enhanced 3D-EKR obtaining a peak value electric current. The EK1, EK2, EK3 and EK4 obtained a high EOF volume at the end of three-dimensional electrokinetic remediation, which were 201, 221, 226 and 231 mL, respectively. The anolyte and catholyte electrical conductivity of EK1, EK2, EK3 and EK4 obtained peak value at the initial of the electrokinetic remediation, and the anolyte of EC were 2546, 3215, 3467 and 3865 μS cm−1 in the EK1, EK2, EK3 and EK4 treatments, respectively, and catholyte of EC were 2987, 3689, 3876 and 4578 μS cm−1 in the EK1, EK2, EK3 and EK4 treatments, respectively. MGDA, HIDS and PASP enhance Cu, Zn, Cr and Pb removing from contaminated soil during the three-dimensional electrokinetic remediation process. In the EK4 remediation test, Cu, Zn, Cr and Pb removal efficiencies were 78.6 ± 2.41%, 82 ± 2.45%, 85.8 ± 2.89%, 70 ± 2.87%, respectively. MGDA, HIDS and PASP could enhance exchangeable and reducible fractions of Cu removal from contaminate soil during the 3D-EKR process.
Wastewater treatment processes are characterized by strong disturbances and pronounced coupling among multiple control loops, while the effluent quality (EQ) and operational energy consumption (EC) are subject to inherent trade-offs. Conventional control strategies based on fixed setpoints and passive responses have difficulty achieving a dynamic optimal balance between EQ and EC in response to variations in the water quality and operating conditions. To address this issue, this study proposes a multi-objective optimization control method for wastewater treatment processes based on predictive event-triggering and Q-learning-enhanced nonsingular active disturbance rejection control (PET-QNADRC). First, a predictive event-triggered decision mechanism is developed, in which a fuzzy neural network (FNN) is employed to predict the operating states of the two control loops, and risk assessment indices are constructed to dynamically trigger six priority events, thereby enabling adaptive switching of control strategies. Second, with the minimization of EQ and EC defined as the two optimization objectives, the optimization strategy and objective weights are dynamically adjusted according to the event type. An event-driven multi-objective particle swarm optimization algorithm is then employed to obtain the Pareto-optimal front and generate dynamic optimal setpoint trajectories. Finally, a nonsingular active disturbance rejection controller is designed to actively compensate for inter-loop coupling and external disturbances, while Q-learning is employed to tune the controller parameters online, enabling accurate and disturbance-rejection tracking of the dynamic setpoints. Simulation results demonstrate that PET-QNADRC can effectively track the dynamic optimal setpoint trajectories while improving EQ performance and reducing EC.
Wastewater treatment plants (WWTPs) are complex facilities that must be operated efficiently from both environmental and economic perspectives. They need to balance a range of often conflicting performance objectives, necessitating a plant-wide approach and multi-objective optimization. This paper presents an approach for dynamic plant-wide optimization of the effluent quality index (EQI) and operational cost index (OCI) by employing reduced neuro-fuzzy models to represent performance indices, combined with simplified predictive control. The approach is validated through a one-year simulation using the plant-wide benchmark simulation model no. 2 (BSM2PSFe) and by dynamically optimizing the dissolved oxygen (DO) concentration set-point in an aerobic reactor. The Pareto front of optimal solutions is analyzed, and the individual indices are integrated into a single objective function by adjusting the weights of the criteria. The results show that the proposed optimization approach provides a better trade-off between the EQI and OCI indices than constant DO set-point values. Compared to the DO set-point of 1.5 g/m3, the presented optimization case improves EQI for 2.64% and OCI for 0.87%. The advantage of the approach, apart from plant-wide consideration, is its reliance on measurements available in practice, making it suitable for implementation in real WWTPs.
