OPTIMIZATION OF SUGARCANE PROCESS PRODUCTION USING RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORKS

OKTARINI, DEVIE and Mohruni, Amrifan Saladin and Sharif, Safian and Yanis, Muhammad (2024) OPTIMIZATION OF SUGARCANE PROCESS PRODUCTION USING RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORKS. Doctoral thesis, Sriwijaya University.

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Abstract

The sugarcane industry is crucial for Indonesia's industrial sector but has been declining, leading to increased sugar imports due to insufficient domestic supply. Factors contributing to this decline include the milling process and setup of sugarcane milling machines. Research on sugarcane milling stations' parameters has been conducted multiple times. However, research on clearance that has been carried out only discusses clearance between rollers. Even though the sugarcane that will enter the milling machine also has a clearance. The clearance of sugarcane is thought to influence the production of the amount of sugarcane juice. The first purpose of this study is to determine the effect of clearance of sugarcane, clearance of roller, and speed of roller on the optimization of the sugarcane production process. The second is to determine the optimum settings for the three parameters, and the last determine the optimal amount of sugarcane juice. The approaches used in this research are RSM with the CCD technique and ANNs with the backpropagation algorithm to predict and evaluate the optimum conditions. The results are best model based on the results of the RSM analysis for the mass of sugarcane juice is the quadratic model, the optimum conditions resulting from the prediction and evaluation using RSM are n is 12 rpm, cs are 2.4 cm, and c2 is 1.74 cm with the resulting number of mj is 0.358 kg, then ANNs are n is 12.0 rpm, cs is 2.8 cm, and c2 is 1.74 cm with the quantity of mj produced is 0.360 kg. Top roll rotation (1.60%) is the most significant influence on the mass of sugarcane juice. Followed by the clearance of sugarcane (1.51%). Then, rear clearance (1.06%). The sugarcane process production will be optimal with the condition top roll rotation is lower at n of 12 rpm, the clearance of sugarcane is higher at cs of 2.8 cm, and the rear clearance is lower at c2 of 1.74 cm. The best method for optimization of sugarcane process production is ANNs by the MSE value was 0.000075 and mean % error value was 0.5704.

Item Type:	Thesis (Doctoral)
Uncontrolled Keywords:	Optimization, Sugarcane Process Production, Response Surface Methodology, Artificial Neural Networks
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ1-1570 Mechanical engineering and machinery
Divisions:	03-Faculty of Engineering > 21001-Engineering Science (S3)
Depositing User:	Devie Oktarini
Date Deposited:	24 Sep 2024 02:26
Last Modified:	24 Sep 2024 02:26
URI:	http://repository.unsri.ac.id/id/eprint/157310

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