Neural Network based automated hot water mixture

Abstract

In the present day and age, most residential spaces comprise a shower system and generally a conventional system of hot water showers. Throughout history, showering has developed as an essential need in a person’s life. Nevertheless, a typical hot water shower system comprises delays in hot water mixing and usually requires an average of 2 to 4 minutes to mix the cold and hot water to deliver the appropriate shower temperature. The delay in mixing provides less comfort and poor satisfaction affecting people’s lifestyles. Due to these disadvantages, a system incorporating artificial Intelligence can be utilized to enhance the performance of mixing which can offer an automated hot water mixture system with improved efficiency and effectiveness. Recently, significant research has been focused on utilizing deep learning technology due to its multiple breakthroughs in fabricating a broad range of automated novel applications since Neural Networks comprise the capacity to learn from data to offer efficient and accurate systems. In this research project, the hot water mixture is employed by an Artificial Neural Network model integrated with the combination of an embedded system of the proposed system of hot water mixture. Furthermore, the proposed system comprises temperature and flow sensors along with controllable flow valves. The tested system indicated acceptable accuracy between the actual and desired output flow rate and temperature.