Implementation of Control Strategy for Predicting Energy Consumption Management in a Food and Beverage Industry Using Regression Analysis

The philosophy of efficient energy consumption is vitally crucial to profitable production cost in manufacturing industries. This is because the unit production cost is largely determined by the cost of unit energy supply; which is quite higher than the cost of raw materials in Nigeria. It has been established that the Nigerian industrial sector is responsible for 8.7% of the total energy consumption in the nation. Out of this chunk, the food and beverage industry appropriates approximately 2%. Meanwhile, it is observed that the energy consumption trend in most industrial electric motors is always high due to continuous operation even during the idle time/period in production. In this study, data gathered has a coefficient of determination of 99.7%. This is, thus, subjected to regression analysis which assists in predicting the energy consumption trend for a period of one year. Further to this, the capacity of control principles in efficient energy consumption is demonstrated by practical real time implementation of a smart energy saving in the food industries using PLClogicx software. In this sense, the developed programmable logic control (PLC) ladder diagram was further designed and implemented using fuzzy logic control (FLC). This is simulated using MATLAB/Simulink toolbox. By this arrangement; it is observed that there was a significant reduction in energy consumption. This is obviously revealed in the obtained results. In this case, there was an average electrical energy savings of 65.59% in the plant’s case sealing section while an energy saving of approximately 0.13% was achieved in reference to the overall energy consumption of the industrial plant’s processes. Finally, based on the mathematical calculations obtained from observations of typical production processes in the multinational food and beverage company, the FLC is discovered to provide 99.83% efficiency in optimizing energy consumption.