Enhancing industrial sustainability in complex production systems through energy hotspot identification: A multi-task learning with layer-wise relevance propagation approach

With growing concerns about global warming and environmental degradation, the petrochemical industry has been urged to reduce energy consumption and emissions. However, making operational adjustments in multi-sectional production systems, which involve multiple production stages, is challenging without identifying the areas within the process that are intensive energy consumers. Therefore, this paper proposes a novel hotspot identification methodology in a multi-sectional production system using layer-wise relevance propagation of a multi-task learning model. The proposed model tracks overall energy efficiency as the final output to determine the energy gap, while the activation result identifies areas of production where energy is ineffectively consumed using sectional energy efficiency. The performance of the proposed method is validated using a case study of the vinyl chloride monomer process, which can be divided into five sections. The results show that the proposed methodology effectively captures the relationship between process variables and both tasks (overall and sectional energy efficiency) with an average R 2 of 0.9818 and 0.9858 in prediction with unseen data, respectively. The layer-wise relevance propagation result demonstrates the contribution of unit operations to overall energy efficiency values by tracing the regression trajectories. Furthermore, operational adjustment using the overall energy gap as a benchmark condition offers a significant energy consumption reduction of around 15.87 %, reduces operating expenses for utilities by 147,000 USD, and decreases direct carbon dioxide emissions by 4700 tons, helping production to become more energy-efficient and sustainable. • Real-time industrial hotspot analysis of a multi-sectional system is proposed. • Capturing overall SEC while constrained by sectional SEC using multi-task learning. • Layer-wise relevance propagation tracks prediction gaps to trace energy wastage. • MTL-LRP activation analysis accurately identifies process hotspot locations. • The model uncovers 15 % of energy-saving and reduces 4700 tons of GHG emissions.