Industrial activated sludge model identification using hyperparameter-tuned metaheuristics

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2024-09-20 DOI:10.1016/j.swevo.2024.101733

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Abstract

This study focuses on the parameter estimation of an industrial activated sludge model using hyperparameter-tuned metaheuristic techniques. The data used in this study were collected on-site from a textile industry wastewater treatment plant. A Modified Activated Sludge Model (M-ASM) was the 'first-principle model’ selected and implemented with suitable assumptions. Advanced metaheuristic techniques, as Adaptive Tunicate Swarm Optimization (ATSO), Whale Optimization Algorithm (WOA), Rao-3 Optimization (Rao-3) and Driving Training Based Optimization (DTBO) were implemented. The hyperparameter tuning was performed with Bayesian Optimization (BO). Optimized metaheuristic algorithms were implemented for model-parameter identification. The Bayesian optimized Rao-3(BO-Rao-3) algorithm provided the best validation results, with a Mean Absolute Percentage Error (MAPE) value of 7.0141 and Normalized Root Mean Square Error (NRMSE) value of 0.2629. It also had the least execution time. BO-Rao-3 is 0.93% to 4.7% better than the other implemented hyperparameter-tuned metaheuristic techniques.

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利用超参数调整元搜索法识别工业活性污泥模型

本研究的重点是利用超参数调整元启发式技术对工业活性污泥模型进行参数估计。本研究使用的数据是从一家纺织工业污水处理厂现场收集的。修正的活性污泥模型（M-ASM）是 "第一原理模型"，并在适当的假设条件下实施。采用了先进的元启发式技术，如自适应调谐蜂群优化算法（ATSO）、鲸鱼优化算法（WOA）、Rao-3 优化算法（Rao-3）和基于驾驶训练的优化算法（DTBO）。超参数调整采用贝叶斯优化算法（BO）。优化的元启发式算法用于模型参数识别。贝叶斯优化 Rao-3（BO-Rao-3）算法提供了最佳验证结果，其平均绝对百分比误差（MAPE）值为 7.0141，归一化均方根误差（NRMSE）值为 0.2629。它的执行时间也最短。BO-Rao-3 比其他已实施的超参数调整元启发式技术好 0.93% 到 4.7%。

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来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

期刊介绍： Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.