Energy savings bottleneck diagnosis and optimization decision method for industrial auxiliary system based on energy efficiency gap analysis

High-efficiency green development is the inevitable future of industry. As an important component in the industrial field, the industrial auxiliary system has great energy-saving potential but lacks a means of overall energy savings analysis. To address this problem, this paper proposes an energy savings bottleneck diagnosis and optimization decision method based on energy efficiency gap (EEG) analysis. This method entails six energy efficiency indicators for an industrial system that describe the energy savings of the system from multiple perspectives. The gaps between energy efficiency indicators can quantify five energy savings bottlenecks: production management, working mode plan, operation, system design, and technology development. Through self-analysis of a single system and parallel analysis of multiple systems, EEG analysis can combine results to make optimization scheme decisions, diagnose energy savings bottlenecks, realize targeted system energy-saving optimization, and effectively improve energy savings in industrial systems. The EEG analysis method was successfully applied in two cases. The first case involved the auxiliary system of a transmission device; the energy savings reached 20%. The second case involved the auxiliary system of a steel smelting process; the energy savings reached 12%.