Artificial-intelligence-model to optimize biocide dosing in seawater-cooled industrial process applications considering environmental, technical, energetic, and economic aspects.

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
ACS Applied Bio Materials Pub Date : 2024-05-01 Epub Date: 2024-06-10 DOI:10.1080/08927014.2024.2363241
Sergio García, David Boullosa-Falces, David S Sanz, Alfredo Trueba, Miguel Angel Gomez-Solaetxe
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引用次数: 0

Abstract

This research introduces an Artificial Intelligence (AI) based model designed to concurrently optimize energy supply management, biocide dosing, and maintenance scheduling for heat exchangers. This optimization considers energetic, technical, economic, and environmental considerations. The impact of biofilm on heat exchangers is assessed, revealing a 41% reduction in thermal efficiency and a 113% increase in flow frictional resistance of the fluid compared to the initial state. Consequently, the pump's power consumption, required to maintain hydraulic conditions, rises by 9%. The newly developed AI model detects the point at which the heat exchanger's performance begins to decline due to accumulating dirt, marking day 44 of experimentation as the threshold to commence the antifouling biocide dosing. Leveraging this AI model to monitor heat exchanger efficiency represents an innovative approach to optimizing antifouling biocide dosing and reduce the environmental impact stemming from industrial plants.

从环境、技术、能源和经济等方面考虑,建立人工智能模型,优化海水冷却工业流程应用中的杀菌剂剂量。
本研究介绍了一种基于人工智能(AI)的模型,旨在同时优化热交换器的能源供应管理、杀菌剂剂量和维护调度。这种优化考虑了能源、技术、经济和环境因素。通过评估生物膜对热交换器的影响,发现与初始状态相比,热效率降低了 41%,流体的流动摩擦阻力增加了 113%。因此,维持水力条件所需的泵功率消耗上升了 9%。新开发的人工智能模型可以检测到热交换器的性能因污垢积累而开始下降的时间点,并将实验的第 44 天作为开始添加防污杀菌剂的临界点。利用这一人工智能模型来监控热交换器的效率,是优化防污杀菌剂剂量和减少工业厂房对环境影响的创新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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