Optimizing high-speed rotating shaft vibration control: Experimental investigation of squeeze film dampers and a comparative analysis using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM)

Application of the Surface Regression Technique for Enhancing the Input Factors and Responses for Processing Coconut Oil under Vertical Compression

“…In other words, at higher speeds, the viscosity of the oil thus remains lower, resulting in less pressure build-up and more oil content in press cake [13,16,52]. Moreover, the BBD approach is a type of response surface technique used to maximize the response of a process by determining the correlation between the input factors and the measured responses [25,28,29,31,34,38]. For instance, Kandar and Akil [25] studied three independent process variables including the molding temperature, time, and pressure.…”

“…Conventionally, most researchers adopt the classical experimental method where one operating factor is changed at a time while the other factors are kept constant [25,26]. Presently, modeling, and optimization techniques such as the Artificial Neural Networks (ANN) [15,18,27,28], Adaptive neuro_fuzzy inference system (ANFIS) [29,30], and Box-Behnken Design (BBD) coupled with Response Surface Methodology (RSM) [28,29,31] have been employed to reduce the several experiments and time consumption and to maximize cost by determining the optimal processing factors in relation to the responses under investigation. Time consumption is key in oil production and has become a huge obstacle to overcome.…”