Maximization of extraction of Cadmium and Zinc during recycling of spent battery mix: An application of combined genetic programming and simulated annealing approach

Liu, Yun; Li, Wei; Garg, Akhil; Maddila, Sivasriprasanna; Gao, Liang; Fan, Zhun; Buragohain, Poly; Wang, Chin‐Tsan

doi:10.1016/j.jclepro.2018.11.226

Cited by 11 publications

(5 citation statements)

References 20 publications

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“…This serves as an indication for the development of energy management and storage systems that can account for the trends observed during this study. Further research can be performed to accelerate the stabilization of power generation using advanced simulation and optimization methods as discussed in Garg et al, Shui et al, Garg et al, and Yun et al…”

Section: Discussionmentioning

confidence: 99%

A novel method for determination of a time period for stabilization of power generation of microbial fuel cell with effect of microorganisms

et al. 2019

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Summary Microbial fuel cells (MFCs) are quickly gaining traction in the mainstream industry due to their capabilities in simultaneous power generation and wastewater purification. They use bacteria like Shewanella and Geobacter as primary units for the same. However, their power generation capabilities are limited by a lack of stability in certain configurations. For the development of appropriate power storage and management systems, this instability must be investigated. Therefore, the present study proposes the artificial intelligence (AI) methodology of artificial neural search (ANS) networks to predict the period for stabilization of power generation of microbial fuel cell in the presence of microorganisms. An output voltage has been measured as a function of time (approximately 1600 h). A stabilization period of power generation has been predicted from the slope obtained from the graph of voltage vs time. The analysis of the ANS model indicated that the power generation stabilization has occurred between 12th and 16th weeks. Experiments were then performed to validate the findings from the ANS model. This may serve as an indication for the development of energy management and storage systems that can account for the trends observed during this study

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Section: Discussionmentioning

confidence: 99%

A novel method for determination of a time period for stabilization of power generation of microbial fuel cell with effect of microorganisms

et al. 2019

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“…In construction engineering and management research, there are several advantages associated with the utilization of GP against the other types of machine learning-based algorithms or statistical-based techniques, such as linear/non-linear techniques (Yun et al 2019). For instance, the optimized solutions are produced in the GP algorithm without limiting the length of solutions.…”

Section: Genetic Programmingmentioning

confidence: 99%

A Coupled Genetic Programming Monte Carlo Simulation–Based Model for Cost Overrun Prediction of Thermal Power Plant Projects

Islam

Mohandes

Mahdiyar

et al. 2022

J. Constr. Eng. Manage.

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Globally, power projects are prone to cost overrun projects. Within the body of knowledge, previous studies have paid less attention to predicting the cost overruns to assist contingency cost planning. Particularly, the thermal power plant projects (TPPPs), the enormous risks involved in their delivery undermine the accuracy of cost overrun prediction. To prevent cost overrun in thermal plant projects, these risks need to be accounted for by employing sophisticated cost overrun prediction techniques. This study aims to develop a Hybrid Predictive-Probabilistic-based Model (HPPM) that integrates a genetic programming technique with a Monte Carlo simulation (MCS). The HPPM was proposed based on the data collected from TPPPs in Bangladesh. Also, the sensitivity of the HPPM was examined to identify the critical risks in cost overruns simulation.The simulation outcomes show that 40.48% of a project's initial estimated budget was the most probable to cost overrun, while the maximum cost overrun will not exceed 75% with 90% of confidence. Practically, the analysis will sensitize project managers to emphasize thermal plants' budget accuracy not only at the initial project delivery phase but throughout the project lifecycle.Theoretically, the HPPM model could be employed for cost overrun prediction in other types of power plant projects.

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“…In order to improve the utilization efficiency of used batteries, optimizing the recovery of materials from the batteries is a promising research in the future (Li et al, 2019). Yun et al pointed out that by optimizing battery material extraction technology, the harm of lithium-ion batteries can be alleviated and the development of clean energy storage systems can be promoted (Yun et al, 2019).Therefore, based on the proposed recycling system, more attention should be paid to the recycling technology of battery materials.…”

Section: Conclusion and Perspectivementioning

confidence: 99%

Integrating offline logistics and online system to recycle e-bicycle battery in China

Wang

et al. 2020

Journal of Cleaner Production

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Maximization of extraction of Cadmium and Zinc during recycling of spent battery mix: An application of combined genetic programming and simulated annealing approach

Cited by 11 publications

References 20 publications

A novel method for determination of a time period for stabilization of power generation of microbial fuel cell with effect of microorganisms

A novel method for determination of a time period for stabilization of power generation of microbial fuel cell with effect of microorganisms

A Coupled Genetic Programming Monte Carlo Simulation–Based Model for Cost Overrun Prediction of Thermal Power Plant Projects

Integrating offline logistics and online system to recycle e-bicycle battery in China

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