Application of Data Driven Technology in Wastewater Treatment Process

Xu, Chao; Zhou, Yu; Li, Jinhua; Yang, Xi-Xin

doi:10.1109/isctt51595.2020.00017

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…Wang et al [36] introduced a data-driven approach to the simulation optimization algorithm that can effectively reduce the computational complexity. Chao et al [37] proposed data-driven methods to predict energy consumption and optimize modeling using an adaptive neural fuzzy inference system based on an ACA. Gu et al [38] solved the data-driven constrained multi-objective mixed-integer optimization problem with a Random Forest (RF) classifier being a proxy model to approximate targets and constraints.…”

Section: Algorithms For Solving Multimodal Transportation Modelsmentioning

confidence: 99%

Multi-Objective Optimization for Multimodal Transportation Routing Problem with Stochastic Transportation Time Based on Data-Driven Approaches

Peng

Gao

et al. 2022

SSRN Journal

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Section: Algorithms For Solving Multimodal Transportation Modelsmentioning

confidence: 99%

Multi-Objective Optimization for Multimodal Transportation Routing Problem with Stochastic Transportation Time Based on Data-Driven Approaches

Peng

Gao

et al. 2022

SSRN Journal

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Multi-objective optimization for multimodal transportation routing problem with stochastic transportation time based on data-driven approaches

Peng¹,

Gao²,

Yu³

et al. 2023

RAIRO-Oper. Res.

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We study a multi-objective optimization model of a stochastic multimodal transportation network considering key impact factors such as transit cost, time, and transport mode schedule while minimizing total transportation cost and transportation time. In this study, we apply the Monte Carlo simulation to deal with the stochastic transportation time in the network and propose a data-driven approach that combines historical data and the dataset generated by the data mining algorithm to accelerate the search for the nondominated solution in the simulation. To validate the effectiveness of the proposed Data-Driven Multi-Objective Simulation Ant Colony (DD-MSAC) algorithm, we compare the optimum-seeking performance and the running time consumption of the Nondominated Sorting Genetic Algorithm-II (NSGA-II) and the Multi-Objective Simulation Ant Colony (MSAC) algorithm. Then, the MSAC algorithm is adopted as the benchmark for the comparison study on the solving performance of the proposed DD-MSAC algorithm. We conducted 30 times simulation run under different network scales in our numerical examples to show that the DD-MSAC algorithm can be equally effective as the non-data-driven MSAC algorithm in finding a nondominated solution as the average error does not exceed 5%. Meanwhile, we analyze the impact of different data-driven approaches, including data pool and support vector machine, on the solution quality and the running time. Finally, we use an example of China’s Belt Road Initiative to verify the effectiveness of the proposed algorithm.

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Energy efficiency optimization of water pumps based on particle swarm algorithm*

Huang,

Zhao,

Yang

2023

2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

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Application of Data Driven Technology in Wastewater Treatment Process

Cited by 4 publications

References 7 publications

Multi-Objective Optimization for Multimodal Transportation Routing Problem with Stochastic Transportation Time Based on Data-Driven Approaches

Multi-Objective Optimization for Multimodal Transportation Routing Problem with Stochastic Transportation Time Based on Data-Driven Approaches

Multi-objective optimization for multimodal transportation routing problem with stochastic transportation time based on data-driven approaches

Energy efficiency optimization of water pumps based on particle swarm algorithm*

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