Comparison between artificial neural network and response surface methodology in the prediction of the parameters of heat set polypropylene yarns

Abstract

In the present paper, a response surface model has been introduced to predict the geometrical parameters of heat set polypropylene pile yarns. The input factors of the presented model include yarn twist, initial yarn count, time, and temperature of heat setting and the response factors are yarn count, yarn shrinkage, crimp contraction and packing factor after the heat setting process. To analyse the effect of this process on the yarn parameters, the dry heat setting process has been applied to all samples at different times and temperatures using an oven equipped with air circulation because of better accuracy and control of temperature. The obtained results showed that there is a positive relation between time and temperature and output parameters. Finally, the predicting equations discussions about the optimum points for maximum shrinkage and interactions of parameters have been presented. Hence, due to some disability of the RSM method, an ANN model has been designed to predict the parameters at higher accuracy. The results of the accomplished ANN model represent a higher prediction correlation coefficient compared to RSM.

Organic cherry laurel (Prunus laurocerasus) vinegar enriched with bioactive compounds with ultrasound technology using artificial neural network (ANN) and response surface methodology (RSM): Antidiabetic, antihypertensive, cytotoxic activities, volatile profile and optical microstructure

“…ANN is also useful, as it is flexible for adding new experimental data for model generation. But it has more skills for calculating multiple responses in a single study than RSM (Dadgar et al, 2015). Therefore, in our study, it was concluded that the ANN model is more reliable and accurate in terms of predictive ability and compliance with the…”

“…Recently, the artificial neural network (ANN) has attracted attention as a nonlinear computational model tool used in modeling and optimization of food processes. ANN is designed as a prediction tool that can simulate various systems and has higher efficiency, accuracy, and flexibility in experimental data fitting, nonlinear correlation estimation compared to RSM (Dadgar et al, 2015;Said et al, 2020;Teslić et al, 2019;Yang et al, 2019). There is no study comparing the ANN and RSM models of ultrasound treatment of vinegar and optimization of its bioactive components.…”

Optimization of the processing technology of schizonepetae herba carbonisata using response surface methodology and artificial neural network and comparing the chemical profiles between raw and charred schizonepetae herba by UPLC-Q-TOF-MS

“…Therefore proceeding with the optimization of the fitted response could yield poor or misleading results. The results suggested that the model could make adequate predictions within the range of the variables employed [ 21 ].…”

Unraveling Biotic and Abiotic Factors Shaping Sugarcane Straw Polyphenolic Richness: A Gateway to Artificial Intelligence-Driven Crop Management

“…The RSM requires numerous runs under a standard experimental design for multi-response optimization. However, the ANN can calculate multiple responses in a single run and is independent of the experimental design [41]. To optimize the harvesting conditions of sugarcane to produce the straw extract with a high content of phenolics, the ANN architecture is therefore superior to the RSM model in terms of predictability.…”