Modeling and optimization of electrodeionization process for the energy-saving of ultrapure water production

Qian, Fengting; Lu, Jiaqi; Gu, Dungang; Li, Guanghui; Liu, Yang; Rao, Pinhua; Fang, Shunong; Zhang, Nan

doi:10.1016/j.jclepro.2022.133754

Cited by 17 publications

(4 citation statements)

References 31 publications

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“…Mathematical modelling assisted by ML is a promising strategy to solve this type of complex problem (with multi-dimensional variables) because it quantifies the influence of process variables (input features) on material recovery efficiency (predicted output). The non-linear model can be optimised to suggest an energy-saving, environmentally-friendly, economically-feasible design for emerging recycling technologies 43 , which is the ultimate target of our research. However, for the current result shown in Fig.…”

Section: Discussionmentioning

confidence: 99%

Neural network based prediction of the efficacy of ball milling to separate cable waste materials

Han

Kumagai

et al. 2023

Commun Eng

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Material recycling technologies are essential for achieving a circular economy while reducing greenhouse gas emissions. However, most of them remain in laboratory development. Machine learning (ML) can promote industrial application while maximising yield and environmental performance. Herein, an asynchronous-parallel recurrent neural network was developed to predict the dynamic behaviour when separating copper and poly(vinyl chloride) components from the cable waste. The model was trained with six datasets (treatment conditions) at 3600 epochs. High accuracy was confirmed based on a mean-square error of 0.0015–0.0145 between the prediction and experimental results. The quantitative relationship between the input features and the separation yield was identified using sensitivity analysis. The charged weight of cables and impact energy were determined as the critical factors affecting the separation efficiency. The ML framework can be widely applied to recycling technologies to reveal the process mechanism and establish a quantitative relationship between process variables and treatment outputs.

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Section: Discussionmentioning

confidence: 99%

Neural network based prediction of the efficacy of ball milling to separate cable waste materials

Han

Kumagai

et al. 2023

Commun Eng

Self Cite

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“…In our previous study, based on a dilute chamber in the EDI module, a process model of UPW preparation by EDI was established in GAMS (the General Algebraic Modeling System) with a two-dimensional finite element method. The detailed mechanism of the process model is included in the Supporting Information, Section S3, and more modeling details can be found in our previous article [32].…”

Section: Modeling Of Edi Modulesmentioning

confidence: 99%

“…The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/su142315957/s1 [32,42,43].…”

Section: Supplementary Materialsmentioning

confidence: 99%

“…The purpose of this study is to suggest the low-carbon design and operation of the EDI system, considering the flexible load regulation of thermal plants. This study applied a two-dimensional finite element model of the UPW preparation process by EDI established in our previous work [32]. First, EDI technology and the EDI system are introduced in Sections 2.1 and 2.2, and the modeling method of EDI systems and modules are described in detail in Sections 2.3 and 2.4.…”

Section: Introductionmentioning

confidence: 99%

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Design Optimization and Carbon Footprint Analysis of an Electrodeionization System with Flexible Load Regulation

Yuan

Qian

et al. 2022

Sustainability

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Thermal power plants will function as a flexible load regulation in a low-carbon grid, which requires operation adaption for the whole system. Energy transition in the electricity sector is the core to realizing carbon neutrality. The power grid will be gradually dominated by renewable energy, such as wind power and photovoltaic solar power. However, renewable energy has problems such as insufficient power supply and output fluctuation; thermal power will be required to regulate the peak load flexibly to meet demand. Therefore, the supply of boiler make-up water prepared by electrodeionization (EDI) in thermal power plants should also be flexibly changed. This study focused on the ultrapure water preparation system by EDI with variable flow rates. For an EDI system with a maximum ultrapure water capacity of 20 m3/h, the power consumption, annual cost, and carbon footprint of different designs are compared. The operation parameters were optimized based on the optimal cost design when the temporal demand of boiler make-up water is reduced to 75%, 50%, and 25%, respectively, considering thermal power as peak load regulation technology. The results showed that the optimized system could significantly reduce power consumption and carbon footprint by up to 30.21% and 30.30%, respectively. The proposed strategy is expected to be widely applied for design and operation optimization when considering the low-carbon but unstable energy system dominated by renewable energy. The carbon footprint could be a feasible optimization object to balance the greenhouse gas emissions from the module manufacturing and operation consumption.

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Configuration and mechanism of electrodeionization module

Kumar,

Rathi

2024

Electrodeionization

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Modeling and optimization of electrodeionization process for the energy-saving of ultrapure water production

Cited by 17 publications

References 31 publications

Neural network based prediction of the efficacy of ball milling to separate cable waste materials

Neural network based prediction of the efficacy of ball milling to separate cable waste materials

Design Optimization and Carbon Footprint Analysis of an Electrodeionization System with Flexible Load Regulation

Configuration and mechanism of electrodeionization module

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