Multi-period analysis of heat integration measures in industrial clusters

Abstract

TSA (total site analysis) has shown to be an efficient tool for identifying heat integration measures in industrial clusters, leading to the optimal design of utility systems and energy bill reduction. In order to justify investments, any proposed utility system must be shown to be able to operate in all configurations that an industrial cluster can encounter, especially those relating to varying heat demand. Previous TSAs have generally been carried out using yearly means of heat exchange loads or using scenarios corresponding to specific operation modes of the sites. While these have been useful for designing systems under normal conditions, they are not fit for evaluating minima and peaks in utility demand. Carrying out a TSA on each possible configuration of a cluster is not feasible from a computational and results analysis point of view. A method is therefore proposed to represent the variability of data over long periods in a reduced form in order to carry out engineering studies.A methodology is proposed to identify typical operating periods of an industrial cluster made up of several production units. This algorithm exploits a multi-objective optimisation to identify n periods that delimit typical operating modes or multiple profiles.A TSA was previously carried out on the Stenungsund petrochemical cluster in Sweden, leading to the design of a utility system to significantly reduce the overall energy consumption of the cluster. The solution proposes that a common utility system would decrease the hot utility demand from 124 MWth to 70 MWth. The multi-period analysis methodology is demonstrated by application to this case study in order to identify the resilience of the proposed solution when faced with variations in heat production and consumption. The multi-period analysis of the proposed utility system leads to the identification of a peak utility demand of 88 MWth rather than the previously identified 70 MWth. A Total Site Sensitivity Analysis leads to a better understanding of the contribution of each of the clusters units and feasibility of investments.