Toward optimized operation of freshwater generator using computer vision, and its economic and environmental benefits

Abstract

Sufficient quantities of fresh water (FW) must be maintained for the operation of ships and fresh water generators (FWGs), used to produce FW on ships tend to be suboptimal because of work schedules and low-skilled marine crew. Therefore, a system that optimally and automatically operates the discharge valve of an FWG distillate pump by recognizing the FW condition through a sight glass using computer vision technology was developed. Three deep-learning models were trained using videos of FW flowing and filling the sight glass. An optimal model structure was selected in the pre-test phase, which consisted of (i) a test using a camera and a monitor showing the validation videos, and (ii) a test based on data. In the real-test phase, an electrical valve and stepper valve were used as optimization valves, and the flow rates of the fuel oil for the generator engine (G/E) and FW for the FWG were recorded for 10 experimental conditions. The stepper valve operated in a more stable manner than the electrical valve, and its prediction performance with CNN-LSTM was superior. Finally, the economic and environmental impacts were evaluated based on the flow rates of fuel oil for the G/E and FW for the FWG.