Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems
Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems
复制标题
在为流程制造系统开发数据驱动模型时的考量、挑战与机遇
DOI：
10.1016/j.compchemeng.2020.106881
复制
发表时间：
2020-09-02
影响因子：
4.3
通讯作者：
Gomes, Rachel L.
中科院分区：
文献类型：
--
作者：
Fisher, Oliver J.;Watson, Nicholas J.;Gomes, Rachel L.
由于采用了低成本的工业物联网技术，数据的可用性不断提高，再加上云计算的处理能力不断增强，推动了制造业中更多地使用数据驱动的模型。利用食品饮料行业和废物管理行业的案例研究，探讨了为制造系统开发数据驱动模型时面临的考虑因素和挑战。确保一组高质量的模型开发数据准确地表示制造系统是成功开发数据驱动模型的关键。跨行业标准流程数据挖掘(CRISP-DM)框架用于提供参考，说明流程制造商在开发数据驱动模型时将面临的独特考虑和挑战。然后，本文探讨了如何利用数据驱动模型来描述流程，并支持循环经济原则、流程弹性和废物定价的实施。(C)2020作者。爱思唯尔有限公司出版。
The increasing availability of data, due to the adoption of low-cost industrial internet of things technologies, coupled with increasing processing power from cloud computing, is fuelling increase use of data-driven models in manufacturing. Utilising case studies from the food and drink industry and waste management industry, the considerations and challenges faced when developing data-driven models for manufacturing systems are explored. Ensuring a high-quality set of model development data that accurately represents the manufacturing system is key to the successful development of a data-driven model. The cross-industry standard process for data mining (CRISP-DM) framework is used to provide a reference at to what stage process manufacturers will face unique considerations and challenges when developing a data-driven model. This paper then explores how data-driven models can be utilised to characterise process streams and support the implementation of the circular economy principals, process resilience and waste valorisation. (C) 2020 The Authors. Published by Elsevier Ltd.