Using Field Data for Energy Efficiency Based on Maintenance and Operational Optimisation. A Step towards PHM in Process Plants

Energy saving is an important issue for any industrial sector; in particular, for the process industry, it can help to minimize both energy costs and environmental impact. Maintenance optimization and operational procedures can offer margins to increase energy efficiency in process plants, even if they are seldom explicitly taken into account in the predictive models guiding the energy saving policies. To ensure that the plant achieves the desired performance, maintenance operationsandmaintenanceresultsshouldbemonitored,andtheconnectionbetweentheinputsand theoutcomesofthemaintenanceprocess,intermsoftotalcontributiontomanufacturingperformance, should be explicit. In this study, a model for the energy efficiency analysis was developed, based on cost and benefits balance. It is aimed at supporting the decision making in terms of technical and operationalsolutionsforenergyefficiency,throughtheoptimizationofmaintenanceinterventionsand operational procedures. A case study is here described: the effects on energy efficiency of technical and operational optimization measures for bituminous materials production process equipment. The idea of the Conservation Supply Curve (CSC) was used to capture both the cost effectiveness of the measures and the energy efficiency effectiveness. The optimization was thus based on the energy consumption data registered on-site: data collection and modelling of the relevant data were used as a base to implement a prognostic and health management (PHM) policy in the company. Based on the results from the analysis, efficiency measures for the industrial case study were proposed, also in relation to maintenance optimization and operating procedures. In the end, the impacts of the implementation of energy saving measures on the performance of the system, in terms of technical and economic feasibility, were demonstrated. The results showed that maintenance optimization could help in reaching an energy costs recovery equal to the 10% of the total costs for an electric motor system.