Scoping and Identifying Data-Driven Optimization Prospects in the Danish Processing Industry
Published in Computer Aided Chemical Engineering, 2024
Increased investment in various technological upgrades (sensors and IT infrastructures) in the processing industry has resulted in large data lakes over the last decade. These can be leveraged for various purposes such as gaining further process insight and optimization through advanced analytics and data-driven modelling. Nevertheless, this potential is rarely fulfilled due to the required wide range of expertise and time constraints. This work presents an extended framework towards bridging the gap between data availability and data utilization for the process and energy optimization within the Danish processing industry. The overall project aims to produce a real-world implementation of data-driven methods on-site at five different industries to highlight the potential, opportunities, and barriers of such endeavours. The multifaceted nature of the project requires multidisciplinary teams with various expertise and a revised systematic framework to cover the complete life cycle of a data-driven model, from scoping the problem and model development to the on-site deployment and contentious maintenance. The core phases of such a framework are proposed herein.
Recommended citation: Aouichaoui, A. R., Ernstsson, B. B., Jul-Rasmussen, P., Iversen, N. H., Vermue, L., & Huusom, J. K. (2024). Scoping and Identifying Data-Driven Optimization Prospects in the Danish Processing Industry. In Computer Aided Chemical Engineering (Vol. 53, pp. 451-456). Elsevier. https://doi.org/10.1016/B978-0-443-28824-1.50076-4