Optimal control for wastewater treatment process based on an adaptive multi-objective differential evolution algorithm

Development and application of an intelligent nitrogen removal diagnosis and optimization framework for WWTPs: Low-carbon and stable operation

Optimizing nitrogen removal is crucial for ensuring the efficient operation of wastewater treatment plants (WWTPs), but it is susceptible to variations in influent conditions and operational parameter constraints, and conflicts with the energy-saving and carbon emission reduction goals. To address these issues, this study proposes a hybrid framework integrating process simulation, machine learning, and multi-objective genetic algorithms for nitrogen removal diagnosis and optimization, aiming to predict the total nitrogen in effluent, diagnose nitrogen over-limit risks, and optimize the control strategies. Taking a full-scale WWTP as a case study, a process time-lag simulation-enhanced machine learning model (PTLS-ML) was developed, achieving R2 values of 0.94 and 0.79 for the training and testing sets, respectively. The proposed model successfully identified the potential reasons of nitrogen over-limit risks under different influent conditions and operational parameters, and accordingly provided optimization suggestions. In addition, the multi-objective optimization (MOO) algorithms analysis further demonstrated that maintaining 4-6 mg/L total nitrogen concentration in effluent by adjusting process operational parameters can effectively balance multiple objectives (i.e., effluent water quality, operating costs, and greenhouse gas emissions), achieving coordinated optimization. This framework can serve as a reference for stable operation, energy-saving, and emission reduction in the nitrogen removal of WWTPs.

Indicator based multi-criteria decision support systems for wastewater treatment plants

Wastewater treatment plant decision makers face stricter regulations regarding human health protection, environmental preservation, and emissions reduction, meaning they must improve process sustainability and circularity, whilst maintaining economic performance. This creates complex multi-objective problems when operating and selecting technologies to meet these demands, resulting in the development of many decision support systems for the water sector. European Commission publications highlight their ambition for greater levels of sustainability, circularity, and environmental and human health protection, which decision support system implementation should align with to be successful in this region. Following the review of 57 wastewater treatment plant decision support systems, the main function of multi-criteria decision-making tools are technology selection and the optimisation of process operation. A large contrast regarding their aims is found, as process optimisation tools clearly define their goals and indicators used, whilst technology selection procedures often use vague language making it difficult for decision makers to connect selected indicators and resultant outcomes. Several recommendations are made to improve decision support system usage, such as more rigorous indicator selection protocols including participatory selection approaches and expansion of indicators sets, as well as more structured investigation of results including the use of sensitivity or uncertainty analysis, and error quantification.

Modeling and optimizing of an actual municipal sewage plant: A comparison of diverse multi-objective optimization methods

The activated sludge process of an actual municipal sewage treatment plant was systematically modeled, calibrated, and verified in this study. Identified multi-objective optimization (MOO) methods were employed to optimize the process parameters of the validated model, and the optimal MOO algorithm was obtained by comparing Pareto solution sets. The optimization model consisted of three key evaluation indicators (objective functions), which are the average effluent quality (AEQ), overall cost index (OCI), and total volume (TV) of the biochemical tank, along with 12 more process parameters (decision variables). Three optimization algorithms, i.e., adaptive non-dominated sorting genetic algorithm III (ANSGA-III), non-dominated sorting genetic algorithm II (NSGA-II), and particle swarm algorithm (PSO), were adopted using MATLAB. The comparison of these algorithms demonstrated that the ANSGA-III algorithm had better Pareto solution sets under the triple objective optimization, and the effluent quality of COD, TN, NH₄⁺-N, and TP after optimization decreased by 2.22, 0.47, 0.13, and 0.02 mg/L, respectively. Additionally, the simulated AEQ was reduced by 13% compared to the original effluent, and the OCI and TV decreased from 21,023 kWh d^-1 and 17,065 m³ to 20,226 kWh d^-1 and 16,530 m³, respectively. The reported ANSGA-III algorithm and the proposed multi-objective method have a promising ability for energy conservation, emission reduction, and upgrading of municipal sewage treatment plants.

Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review

Differential evolution (DE) is one of the highly acknowledged population-based optimization algorithms due to its simplicity, user-friendliness, resilience, and capacity to solve problems. DE has grown steadily since its beginnings due to its ability to solve various issues in academics and industry. Different mutation techniques and parameter choices influence DE's exploration and exploitation capabilities, motivating academics to continue working on DE. This survey aims to depict DE's recent developments concerning parameter adaptations, parameter settings and mutation strategies, hybridizations, and multi-objective variants in the last twelve years. It also summarizes the problems solved in image processing by DE and its variants.

Dissolved oxygen control strategies for water treatment: a review

Dissolved oxygen (DO) is one of the most important water quality factors. Maintaining the DO concentration at a desired level is of great value to both wastewater treatment plants (WWTPs) and aquaculture. This review covers various DO control strategies proposed by researchers around the world in the past 20 years. The review focuses on published research related to determination and control of DO concentrations in WWTPs in order to improve control accuracy, save aeration energy, improve effluent quality, and achieve nitrogen removal. The strategies used for DO control are categorized and discussed through the following classification: classical control such as proportional-integral-derivative (PID) control, advanced control such as model-based predictive control, intelligent control such as fuzzy and neural networks, and hybrid control. The review also includes the prediction and control strategies of DO concentration in aquaculture. Finally, a critical discussion on DO control is provided. Only a few advanced DO control strategies have achieved successful implementation, while PID controllers are still the most widely used and effective controllers in engineering practice. The challenges and limitations for a broader implementation of the advanced control strategies are analyzed and discussed.

Reinforcement learning-based particle swarm optimization for sewage treatment control

To solve the problem of high-energy consumption in activated sludge wastewater treatment, a reinforcement learning-based particle swarm optimization (RLPSO) was proposed to optimize the control setting in the sewage process. This algorithm tries to take advantage of the valid history information to guide the behavior of particles through a reinforcement learning strategy. First, an elite network is constructed by selecting elite particles and recording their successful search behavior. Then the network is trained and evaluated to effectively predict the particle velocity. In the periodic wastewater treatment process, the RLPSO runs repeatedly according to the optimized cycle. Finally, RLPSO was tested based on Benchmark Simulation Model 1 (BSM1) of sewage treatment, and the simulation results showed that it could effectively reduce the energy consumption on the premise of ensuring qualified water quality. Furthermore, the performance of RLPSO was analyzed using the benchmarks with higher dimension, which verifies the effectiveness of the algorithm and provides the possibility for RLPSO to be applied to a wider range of problems.

Covid-19 shock: Development of strategic management framework for global energy

Energy resources are vital for the economic development of any nation, and they are currently recognised as an essential commodity for human beings. Many countries are facing various levels up to severe energy crisis due to limited natural resources, coupled with the Covid-19 pandemic. This crisis can lead to the shutdown or restriction of many industrial units, limited energy access, exacerbating unemployment, simultaneous impacts on people's lives. The main reason for these problems is the increasing gap between energy supply and demand, logistics, financial issues, as well as ineffective strategic planning issues. Different countries have different visions, missions, and strategies for energy management. Integrated strategic management is requisite for managing global energy. This study aims to develop a strategic management framework that can be used as a methodology for policymakers to analyse, plan, implement, and evaluate the energy strategy globally. A conceptual research method that relies on examining the related literature is applied to develop the framework. The present study yielded two main observations: 1) The identification of key concepts to consider in designing the strategic management framework for global energy, and 2) A strategic management framework that integrates the scope, process, important components, and steps to manage global energy strategies. This framework would contribute to providing a standard procedure to manage energy strategies for policymakers at the global, regional, national, state, city, district, and sector levels.

Modeling, instrumentation, automation, and optimization of water resource recovery facilities (2019) DIRECT

A review of the literature published in 2019 on topics relating to water resource recovery facilities (WRRFs) in the areas of modeling, automation, measurement and sensors, and optimization of wastewater treatment (or water resource reclamation) is presented.