Autonomous driving paper index
Artificial Intelligence in Chemical Process Optimization: Techniques, Applications, Challenges, and Future Directions
One-line summary
Chemical processes are often complex nonlinear reactions, with large numbers of operating variables, and high energy consumption; therefore, optimization is not an easy task.
Engineering notes
Key topics: autonomous driving, reinforcement learning. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Chemical processes are often complex nonlinear reactions, with large numbers of operating variables, and high energy consumption; therefore, optimization is not an easy task. With the industrial data from sensors and monitoring systems becoming more widely available, artificial intelligence (AI) is being deployed to enable process efficiency as well as better decision making. Further, unlike the more traditional optimization methods used, especially for complex and high dimensional systems as in molecular design, AI techniques can do so much more efficiently than exploring the whole computational landscape thanks to their powerful predictive capabilities. This review provides an overview of recent developments in machine learning, deep learning (DL), reinforcement learning, and evolutionary algorithms for chemical process optimization across four key industrial applications: reactor design; distillation; energy management; and predictive maintenance. Results suggest that AI-driven solutions enhance process performance, energy savings and promote sustainable industrialization in the context of SDGs 7, 9, 12 and 13.
Links and sources
Need this topic turned into a technical roadmap?
Full Self Driving can prepare a custom autonomous driving literature review, code map, dataset map, and B2B technology assessment.
Request B2B research
Comments