Economic optimisation of cold production: a matheuristic with artificial neural network approach

In this paper, the economic optimisation of cold storage is studied. A modern cold room is mainly composed of compressors, a tank to store heat-transfer fluid and cold rooms. The main cost is incurred by energy consumption and maintenance. The price of electricity, which is known in advance, varies during the day. Production schedules that entail higher risks of compressor wear, and thus high maintenance costs, have to be avoided. The temperature inside the cold rooms must be maintained within the allowed range, and complex thermodynamic processes make it difficult to predict temperature. The tank has a limited capacity. This paper presents the first model optimising the management cost of a cold store with a tank. Maintenance costs are considered for compressors, and Artificial Neural Networks are used to forecast the temperatures in the cold rooms. An optimal Dynamic Programme is designed for the case with one cold room and a matheuristic algorithm is presented for the general case with several cold rooms. A comparison with a classical hysteresis controller shows significant savings. The impact of storage capacity on operating costs is evaluated, after which the influence of the maintenance cost value is discussed.