Engineering

Perspective | 2021-09-30

Intelligent Manufacturing for the Process Industry Driven by Industrial Artificial Intelligence

Based on the analysis of the characteristics and operation status of the process industry, as well as the development of the global intelligent manufacturing industry, a new mode of intelligent manufacturing for the process industry, namely, deep integration of industrial artificial intelligence and the Industrial Internet with the process industry, is proposed. This paper analyzes the development status of the existing three-tier structure of the process industry, which consists of the enterprise resource planning, the manufacturing execution system, and the process control system, and examines the decision-making, control, and operation management adopted by process enterprises. Based on this analysis, it then describes the meaning of an intelligent manufacturing framework and presents a vision of an intelligent optimal decision-making system based on human–machine cooperation and an intelligent autonomous control system. Finally, this paper analyzes the scientific challenges and key technologies that are crucial for the successful deployment of intelligent manufacturing in the process industry.

Tao Yang , Xinlei Yi et al.

Perspective | 2021-09-30

Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats

Chemical engineers rely on models for design, research, and daily decision-making, often with potentially large financial and safety implications. Previous efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the expectations. In the last five years, the increasing availability of data and computational resources has led to a resurgence in machine learning-based research. Many recent efforts have facilitated the roll-out of machine learning techniques in the research field by developing large databases, benchmarks, and representations for chemical applications and new machine learning frameworks. Machine learning has significant advantages over traditional modeling techniques, including flexibility, accuracy, and execution speed. These strengths also come with weaknesses, such as the lack of interpretability of these black-box models. The greatest opportunities involve using machine learning in time-limited applications such as real-time optimization and planning that require high accuracy and that can build on models with a self-learning ability to recognize patterns, learn from data, and become more intelligent over time. The greatest threat in artificial intelligence research today is inappropriate use because most chemical engineers have had limited training in computer science and data analysis. Nevertheless, machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers.

Maarten R. Dobbelaere , Pieter P. Plehiers et al.

Article | 2021-10-20

The road to sustainable use and waste management of plastics in Portugal

• Portugal recycles 34% of the 40 kg/hab year of plastic packaging waste.

Article | 2021-10-20

What have we known so far about microplastics in drinking water treatment? A timely review

• 23 available research articles on MPs in drinking water treatment are reviewed.

Article | 2021-10-19

Recovery and reuse of floc sludge for high-performance capacitors

• The feasibility of facile fabrication of capacitor from floc sludge is discussed.

Article | 2021-10-19

A review on sustainable reuse applications of Fenton sludge during wastewater treatment