Improving energy efficiency of carbon fiber manufacturing through waste heat recovery: A circular economy approach with machine learning

摘要

There remain major concerns over the increasing use and waste of materials and energy resources in multiple manufacturing sectors. To address these concerns, some manufacturers have begun to align their R&D efforts with the circular economy principles: Reduce, Reuse, Recycle and Replace (RRRR). Focusing on advanced composites manufacturing sector, this paper presents an innovative approach for process design and analysis of a new waste heat recovery system for carbon fiber manufacturing. Namely, the stabilization process is known to be one of the most critical steps in the production of carbon fibers, as it consumes the most energy, has the largest factory footprint, is a complex system composed of many components, and is the largest capital investment within the factory line. The heat recovery system in this step of the manufacturing can notably reduce energy consumption, emission, cost, and conversion time, while aiming to maintain the mechanical properties of the final product. Here, via an actual industry-scale fibre production setting, the energy consumption factors were obtained and used to model the total energy and its balance in the thermal stabilization step. Two machine learning approaches with limited data, Artificial Neural Network and Non-Linear Regression were then constructed to predict the energy consumption. Results suggested that using the recovery system by means of a heat exchanger, can yield over 62.7 kW recovery, corresponding to 64% of total exhausted energy from the entire process. The electric energy consumption was reduced from 73.3 kW to 10.2 kW, which corresponded to an 86% improvement in the total energy efficiency. The model also confirmed that, by preheating the make-up air with the recovered energy, the energy performance index of the thermal stabilization can be increased from 0.08 to 0.44, along with a reduction in the process carbon footprint by 28.5 t/y. This is especially crucial as we are turning on smart digitalisation in manufacturing inspired by industry 4.0 concept with limited data.(c) 2021 Elsevier Ltd. All rights reserved.