Improving energy efficiency in Malawian tea industries using an integrated multi-objective optimlzation method combining IDA, DEA and evolutionary algorithms

Rising energy prices, tough competition on the international market, and strict environmental legislation are key challenges facing process industries in Malawi. Further, the country is currently experiencing energy shortage coupled with increasing prices and is forcing the industries now to look at ways and means of reducing their energy consumption and adopting technologies that result in lowering their energy intensity. Optimizing the energy efficiency of processes is one approach towards solving these problems in the short to medium term. This paper presents a method using index decomposition analysis (IDA), data envelopment analysis (DEA) and evolutionary algorithms for generating efficient frontiers in multi-objective optimization problems. The purpose of DEA is to measure the relative efficiency of decision making units and reflects the various preferences of decision makers. The index decomposition analysis aims at understanding the characteristics that underline changes in energy intensity at factory level. In addition, an evolutionary algorithm is used for directly finding Pareto optimal solutions. In this paper, we propose to combine DEA and EA and search for optimal solutions. Data from three Malawian tea factories has been used to test the effectiveness of the proposed method and the energy performance across the factories has been evaluated.