Energy-saving and low-carbon leather production: AI-assisted chrome tanning process optimization

Reducing CO 2 emissions is a global effort to improve industrial cost efficiency, enhance product compatibility, and fight climate change . For leather-making industry, the optimization of tanning process is crucial to fulfill the goal of low-carbon manufacturing. Artificial intelligence (AI) is an emerging technology for its ability to reduce errors, increase efficiency, and help realizing precise optimization of manufacturing processes. Herein, an AI-assisted method was first proposed to optimize chrome tanning process for energy-saving and carbon reduction. By comparing the performance of four machine learning (ML) algorithms, i.e., multiple linear regression (MLR), polynomial regression (PR), support vector regression (SVR), and backpropagation-artificial neural network (BP-ANN), it was found that machine learning models are superior in identifying the optimization of process parameters (pH and time) which lead to maintain key product quality indexes in the penetration procedure and binding procedure. Out of the four ML models, the BP-ANN algorithm and SVR algorithm are proven to have good predictive ability . Thus, we systematically optimized the chrome tanning process for energy saving, with key product quality as the constraints. The optimal process parameters, namely pickling pH of 2.86, penetration time of 90 min, pH after basifying of 4.08, and binding time of 90 min, were generated by solving the optimization models of the chrome tanning process via the genetic algorithm (GA) with powerful global optimization capability. Finally, it was validated that the optimized process was highly reliable by evaluating the wet blue product performances, such as the evenness of chrome distribution, thermal stability , surface reactivity, morphology, as well as physical and mechanical properties. Notably, compared to conventional chrome tanning, the optimized process will save 8353.33 kgce of energy and reduce 87295.6 kgCO 2 of CO 2 emission in an average tannery with an annual production capacity of 300,000 standard sheets of upper cowhide leather. This approach can precisely optimize conventional leather-making process, providing a fesible way to achieve energy-saving and low-carbon leather production. • The AI-assisted optimization approach for the tanning process was first proposed. • Mathematical models in chrome tanning procedure were developed and evaluated. • The optimization model of tanning process was systematically constructed. • The optimal process parameters were generated by GA and validated via lab testing. • The optimal tanning process can achieve energy saving and carbon reduction.