PAPER making! FROM THE PUBLISHERS OF PAPER TECHNOLOGY INTERNATIONAL ® FROM THE PUBLISHERS OF PAPER TEC Volume 10, Number 1, 2024
Paper quality enhancement and model prediction using machine learning techniques T. KALAVATHI DEVI 1 , E.B. PRIYANKA 2 & P. SAKTHIVEL 3 A machine learning approach demonstrated in the proposed study predicts the parameters involved in paper quality enhancement in real time. To control the steam pressure during paper manufacture, machine learning algorithms have been used to model different parameters such as moisture, caliper, and weight (grammage). The training and testing data sets were obtained to develop several machine learning models through several data from the parameters of the paper-making process. The inputs considered were moisture, weight, and grammage. As a result, the developed model showed better results by showing less execution time, fewer error values such as root mean squared error, mean squared error, mean absolute error, and R squared score. In addition, modelling was carried out based on model interpretation and cross-validation results, showing that the developed model could be a more useful tool in predicting the performance of the steam pressure and input parameters in the paper-making process. A comparison of results shows that the k-Nearest Neighbor algorithm outperforms the other machine learning techniques. Machine learning is also used to predict the efficiency of steam pressure reduction. Contact information: 1. Department of Electronics and Instrumentation Engineering, Kongu Engineering College, Perundurai, Tamil Nadu, India 2. Department of Mechatronics Engineering, Kongu Engineering College, Perundurai, Tamil Nadu, India 3. Department of EEE, Vellalar College of Engineering and Technology, Tamilnadu, India
Results in Engineering 17 (2023) 100950 https://doi.org/10.1016/j.rineng.2023.100950 This is an open access article under the CC BY-NC-ND lice
The Paper Industry Technical Association (PITA) is an independent organisation which operates for the general benefit of its members – both individual and corporate – dedicated to promoting and improving the technical and scientific knowledge of those working in the UK pulp and paper industry. Formed in 1960, it serves the Industry, both manufacturers and suppliers, by providing a forum for members to meet and network; it organises visits, conferences and training seminars that cover all aspects of papermaking science. It also publishes the prestigious journal Paper Technology International ® and the PITA Annual Review , both sent free to members, and a range of other technical publications which include conference proceedings and the acclaimed Essential Guide to Aqueous Coating .
Article 4 – Paper Quality & AI
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