Main content area

A dynamic approach to energy efficiency estimation in the large-scale chemical plant

Zhu, Li, Chen, Junghui
Journal of cleaner production 2019 v.212 pp. 1072-1085
dynamic models, energy costs, energy efficiency, environmental performance, environmental protection, financial economics, learning, prediction, working conditions
With the increasing pressures from the energy price and environmental protection, large-scale chemical plants pay more attention to the implementation of energy efficiency estimation to improve its economic benefit and environmental performance. Because of the stochastic and dynamic characteristics of the actual data, traditional estimation methods fail to satisfy the requirement of real-time evaluation. To cope with this limitation, a novel energy efficiency estimation method combining just-in-time (JIT) learning and subspace model identification (SMI) with noise elimination, called e-JITSMI method, is proposed. First, the state space model is constructed to describe the dynamic performance of production processes. This integration method can select the appropriate sampling data, estimate noise effect, and build the corresponding dynamic model. With the built model, not only are the relationships between production and supplied energy built, but the energy efficiency tendency is also predicted at the next moment. In addition, with the arrival of the new sampling data, the dynamic evaluation model is automatically updated. The effectiveness and accuracy of the proposed method are demonstrated through a practical large-scale chemical process. The results present the average accuracy of energy efficiency prediction can reach 88.9% and the tendency of energy efficiency is 100% correct even if the working conditions change.