An innovative approach combining industrial process data analytics and operator participation to implement lean energy programs: A Case Study

摘要

Energy costs for process-based industries amount to over 380 billion USD per year. Inrntoday’s economic climate, and considering environmental drivers, addressing energy efficiencyrnis critical. Given that a typical plant captures and archives several thousand measurements perrnsecond, the challenge for industry today remains how to extract value from their “Big Data” tornaddress energy efficiency. Continuous improvement programs aligned with lean manufacturingrnprinciples can optimize current assets leading to operational efficiency gains without capitalrninvestments. Combining advanced analytics and machine learning, with a strong involvement ofrnplant staff and operators is key to deploying an Energy Management System (EnMS) compliantrnwith ISO50001 that will help the plant to quickly achieve significant savings. This paper outlinesrnthe critical steps to implement an EnMS for a complex process: to diagnose energy consumptionrnvariability; identify energy consumption baseline; to engage all levels of production staff in rootrncause analysis workshops; and, to implement predictive models for real-time monitoring,rndecision support and performance reporting. The chemicals plant case study presented in thisrnpaper demonstrates how this approach can be applied to optimize steam production andrndistribution through improved operational management. This case realized operational savings ofrnover 640 000 USD or 7000 tonnes of CO2 per year in gas consumption, representing a 15%rnreduction. Experience from this case emphasizes the importance of using plant monitoring to itsrnfull potential together with involvement of plant operators in order to understand key operationalrnpractices and to help promote an energy efficiency culture.