Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems.

• Data-driven models (DDMs) will become widespread across manufacturing. • Paramount to DDMs is the collection of an accurate set of model development data. • Process manufacturers face unique considerations and challenges in collecting data. • These points are presented in the context of the CRISP-DM framework. • This supports the development of DDMs to meet manufacturers' requirements. 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. [ABSTRACT FROM AUTHOR]