Review of Latest Advances in Nature-Inspired Algorithms for Optimization of Activated Sludge Processes

Deepak, M Mathew; Rustum, Rabee

doi:10.3390/pr11010077

Cited by 8 publications

(6 citation statements)

References 69 publications

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“…Currently, commonly used methods for parameter tuning include the particle swar optimization (PSO) algorithm, whale optimization algorithm (WOA), dung beetle optim zation algorithm, etc. PSO [22] has been applied in many fields. The principle is simp The model uses the rectified linear unit (ReLU) function as the activation function after the convolutional layer because it has the advantages of simple derivation and sparsity of the activated data, which can reduce the network overfitting to some extent.…”

Section: Dbo Performance Testingmentioning

confidence: 99%

“…Currently, commonly used methods for parameter tuning include the particle swarm optimization (PSO) algorithm, whale optimization algorithm (WOA), dung beetle optimization algorithm, etc. PSO [22] has been applied in many fields. The principle is simple and easy to implement, but it lacks the dynamic adjustment of speed, and can easily fall into the local optimum, which will lead to low convergence accuracy and difficulty in converging.…”

Section: Dbo Performance Testingmentioning

confidence: 99%

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Research on Power Device Fault Prediction of Rod Control Power Cabinet Based on Improved Dung Beetle Optimization–Temporal Convolutional Network Transfer Learning Model

Ye,

Chen,

Liu

et al. 2024

Energies

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In order to improve the reliability and maintainability of rod control power cabinets in nuclear power plants, this paper uses insulated gate bipolar transistors (IGBTs), the key power device of rod control power cabinets, as the object of research on cross-working-condition fault prediction. An improved transfer learning (TL) model based on a temporal convolutional network (TCN) is proposed to solve the problem of low fault prediction accuracy across operating conditions. First, the peak emitter voltage of an IGBT aging dataset is selected as the source domain failure characteristic, and the TCN model is trained after the removal of outliers and noise reduction. Then, the time–frequency features are extracted according to the characteristics of the target domain data, and the target domain representation data are obtained using kernel principal component analysis (KPCA) for dimensionality reduction. Finally, the TCN model trained on the source domain is transferred; the model is fine-tuned according to the target domain data, and the learning rate, the number of hidden layer nodes, and the number of training times in the network model are optimized using the dung beetle optimization (DBO) algorithm to obtain the optimal network, making it more suitable for target sample fault prediction. The prediction results of this TCN model, the long short-term memory (LSTM) model, the gated recurrent unit (GRU) model, and the recursive neural network (RNN) model are compared and analyzed by selecting prediction performance evaluation indexes. The results show that the TCN model has a better predictive effect. Comparing the prediction results of the TCN-based optimized transfer learning model with those of the directly trained TCN model, the mean square error, root mean square error, and mean absolute error are reduced by a factor of two to three, which provides an effective solution for fault prediction across operating conditions.

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Section: Dbo Performance Testingmentioning

confidence: 99%

Section: Dbo Performance Testingmentioning

confidence: 99%

Research on Power Device Fault Prediction of Rod Control Power Cabinet Based on Improved Dung Beetle Optimization–Temporal Convolutional Network Transfer Learning Model

Ye,

Chen,

Liu

et al. 2024

Energies

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“…According to the processed data and the identified constraints, the ideal technology configuration for each objective should be generated, as stated in step 9. Taking maximize G * 1 as an example, the model is coded in Equation (26).…”

Section: Minimize D =mentioning

confidence: 99%

“…In essence, MOO generates a set of Pareto frontiers that can be time-consuming, particularly when dealing with an infinite number of Pareto solutions in multi-objective mixed integer linear or nonlinear programming problems. Metaheuristic algorithms, including genetic algorithms, particle swarm optimization, and ant colony optimization, have been extensively employed to explore the solution space and improve the solutions in MOO problems, including those related to WWTP planning [26].…”

Section: Introductionmentioning

confidence: 99%

New Optimization Framework for Improvement Sustainability of Wastewater Treatment Plants

Li,

Pang,

et al. 2023

Processes

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Enhancing the sustainability of wastewater treatment plants (WWTPs) is crucial due to their manifold benefits, which encompass environmental preservation, cost reduction, and resource and energy conservation. The achievement of these advantages relies on the careful choice and implementation of retrofit technologies to upgrade WWTPs. However, this decision-making process is intricate, given the trade-offs between the objectives and the inherent decision uncertainties. To address these complexities, this work presents an innovative weighted multi-objective optimization (MOO) framework tailored for WWTP enhancement amid uncertain conditions. This framework comprises two phases. The first phase involves basic definition and information collection through a case-specific assessment, while the second phase includes model formulation and solver optimization, which serves as a generic tool for the weighted MOO problem. In the model formulation, a combined weighting approach that integrates expert opinions and statistical insights is introduced to assign significance to each objective. The solver optimization employs a projection-based algorithm to identify the optimal technology configuration that achieves a satisfactory and balanced improvement across multiple sustainable objectives. By applying this framework to a case plant for retrofit technology selection, the comprehensive sustainability performance, the targeting of discharged pollution, the operational cost, and the GHG emissions improved by 46.7% to 68.3%.

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“…Bio-inspired optimization algorithms [1,2] are a set of optimization algorithms inspired by natural phenomena, such as evolutionary processes, social behaviours, and swarm intelligence [3]. These algorithms attempt to simulate these processes to solve optimization problems [4,5].…”

mentioning

confidence: 99%

Bio-Inspired Optimization in Engineering and Sciences

Zhang¹,

Chen²

2023

Computer Modeling in Engineering &Amp; Sciences

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Review of Latest Advances in Nature-Inspired Algorithms for Optimization of Activated Sludge Processes

Cited by 8 publications

References 69 publications

Research on Power Device Fault Prediction of Rod Control Power Cabinet Based on Improved Dung Beetle Optimization–Temporal Convolutional Network Transfer Learning Model

Research on Power Device Fault Prediction of Rod Control Power Cabinet Based on Improved Dung Beetle Optimization–Temporal Convolutional Network Transfer Learning Model

New Optimization Framework for Improvement Sustainability of Wastewater Treatment Plants

Bio-Inspired Optimization in Engineering and Sciences

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