ProcessCarbonAgent: A large language models-empowered autonomous agent for decision-making in manufacturing carbon emission management

Abstract

Knowledge-intensive production represents a primary trend in industrial manufacturing, which heavily relies on the production logs of large-scale, historically similar orders for enhancing production efficiency and process quality. These logs are essential for predicting resource allocation and identifying bottlenecks in throughput. As a result, root cause analysis of the production process state is crucial for supporting decision-making in these settings. However, current methodologies heavily depend on expert knowledge, making the analysis time-consuming and inefficient for large-scale, multivariable processes. Although the development of large language models and autonomous agents presents a potential solution, these models are limited in their direct interaction with event logs due to inadequate data representation, token constraints, and insufficient accuracy. Therefore, enabling the interactive capabilities of large language models to overcome these specific limitations in process event data and industrial domain illusions poses a significant challenge. To address these issues, this paper introduces the ProcessCarbonAgent framework, an autonomous agent empowered by large language models, designed to enhance decision-making within industrial processes. Initially, a process data agent combines predefined semantic text representation methods with process template prompting strategies to improve interaction capabilities. Subsequently, an intention agent utilizing self-information and large language models is developed to address context length limitations by identifying and eliminating redundancies. Finally, a two-stage confidence estimation method is implemented to refine the precision of decision-making assistance, thereby improving the accuracy of decisions supported by large language models. Experiments with textile industry carbon emission data reveal that the assisted decision-making scores employing a compression ratio of 0.5, closely align with scores from manually labeled evaluations, with a 98% overlap across scoring intervals. Moreover, in contrast to relying solely on the original evaluation method, the two-stage confidence estimation method has led to a 20% increase in accuracy performance. The ProcessCarbonAgent achieved scores of 16.64, 55.13, 26.32, and 34.17 on METEOR, BERTScore, NUBIA, and BLEURT, respectively. The results demonstrate that the ProcessCarbonAgent framework significantly enhances the decision-making process for high-carbon emission states in industrial production, providing technical support for the low-carbon transformation and intelligent upgrading of these processes.