Work

Energy-efficient and quality-focused control of conveyor belt dryers in petrochemical production

Year: 2024

Type: article

Abstract: The petrochemical industry’s dryers, which operate at high temperatures, consume substantial energy and require precise temperature control to maintain product quality. To address these challenges, conveyor belt dryers are used for continuous drying, but optimizing their operation is complex. Traditional control methods often rely on operator experience or fixed settings, which may not be optimal. This paper introduces a novel approach using a Graph Neural Network and Multiagent Deep Deterministic Policy Gradient algorithm. This method models dryers’ components—chambers, fans, and belts—as cooperating agents, optimizing temperature, fan speed, and belt speed based on quality predictions. The algorithm, trained with industrial data, improves decision-making by integrating quality predictions into its reward function. Extensive experiments show a 5% improvement in product quality and a 3% reduction in energy consumption per time step, translating to significant energy savings. This approach enhances both efficiency and product quality control in conveyor belt dryers.

Source: npj Advanced Manufacturing

DOI: 10.1038/s44334-024-00010-z

Authors: Muhammad Waseem, Kshitij Bhatta, Chen Li, Nabeel Haider, Qing Chang

Institution: University of Virginia

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Responsible consumption and production, Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $2,490