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Energy management systems are considered as appropriate instruments to identify and realize energy efficiency potentials in industrial plants. Studies point out a largely untapped potential for energy efficiency in industry through the adoption of systematic energy management. We suggest introducing qualitative and quantitative modelling tools into energy management to facilitate a focused process to set priorities and identify effective measures. This paper describes the case of voestalpine Austria Draht GmbH, an Austrian wire rolling mill. voestalpine Austria Draht GmbH at present is implementing a knowledge based energy management system. Within the work described in this paper, first an overview of energy flows using Sankey diagrams was elaborated. Then a qualitative systems analysis was developed for the plant and a gap analysis done for priority areas. Subsequently physical input–output models for several energy intensive units were prepared. The paper describes the models for the cooling water system and product cooling. This study revealed a significant potential to save electricity by optimizing the control of cooling water pumps along the milling train and to recover heat from the final products. 2 GWh of electricity can be saved annually. The biggest single energy loss in the mill is the heat from air cooling the products on hook conveyors. These heat flows contain about 30–50 % of the energy input for heating up the billets. Following a theoretical analysis of the potential, a demonstration plant was developed and pilot tests were run to characterize the realistic potential of heat recovery on the hook conveyor. Based on the test results, feasibility studies for large scale plants were carried out. As a result, 0.8 GWh of heat can be recovered annually. The method was found useful because it helped to structure the existing information with a reasonable effort mostly by the operators themselves in their work and to build a conceptual model of energy flows and energy efficiency of the total plant. [ABSTRACT FROM AUTHOR]

Abstract: It is getting more and more popular to apply heuristic optimization methods, like genetic algorithm (GA) and particle swarm optimization (PSO), to handle various engineering optimization problems. In this paper, optimization problems of typical centralized air-conditioning systems were solved by the non-revisiting (Nr) strategy, which was proposed to be incorporated into the common heuristic methods for improving the optimization effectiveness and reliability. This approach can store the evaluated fitness values in an archive with minimal computer memory, detect the revisits and prevent them from re-evaluating. It is particularly useful for the problems formulated by dynamic simulation or detailed modeling with very demanding computational time for function evaluation. The non-revisiting strategy can facilitate the search of the global optimum by its parameter-less adaptive mutation capability. In the optimization problems of central air-conditioning systems, it was found that the NrGA and NrPSO could search better solutions at a limited number of function evaluations than the conventional GA and PSO did. The ultimate goal is to determine the required parameters for optimal design and energy management. The proposed strategy can be applied to similar types of air-conditioning or engineering optimization problems, and possibly incorporated into other kinds of heuristic optimization methods. [ABSTRACT FROM AUTHOR]

Energy optimization is the most important to improve the lifetime of a wireless sensor network. Nodes in sensor networks require to have an optimal mechanism for utilizing energy A new technique named Hybrid Optimization Algorithm, presented in this paper, is based on Lagrangian relaxation and entropy for reducing the energy consumption. The proposed model helps to maintain multi-hop connectivity with dynamic action. The nodes are allowed to communicate based on energy, bandwidth, neighbor connectivity, hop count, to maintain high QoS energy parameters. The simulations are in NS2 software. The proposed approach evaluates total consumed energy, average remaining energy, packets received rate, drop rate, normalized routing overhead, delay, throughput, goodput, jitter, and network lifetime. The comparison results show that the nodes' lifetime improved with a Hybrid Optimal Based Cluster Formation (HOBCF) algorithm. [ABSTRACT FROM AUTHOR]