Abstract
Chemical engineering is a complicated combination of skills that depends on the curricula, teachers, and teaching environment and it requires huge resources to prepare skilled personnel for chemical and related industries. Chemical plant specialists face new challenges as digital and energy transition, new products and processes, circularity paradigm, etc. All these need awareness of new trends and the capability to solve related industrial problems. This paper presents a computer-aided process engineering-based (CAPE-based) curricula development that supports continuous improvements of chemical plants and speeds up the application of up-to-date knowledge directly to industrial practice. The approach is based on the interplay of engineers, plant managers and university professors in solving the real industrial problem. The procedure is supported by a cloud-based CAPE environment for teaching, training, and producing the project results. The industrial problems are identified by the plant personnel and appropriate engineers are assigned by managers. The specific structure of the curricula presumes face-to-face, online and hybrid training and consulting in a triangle engineer, plant supervisor, and professor to solve a problem in the chemical and petrochemical industry. The pool of university professors is adjusted to cover all necessary tasks, provide up-to-date knowledge, and give a new paradigm to engineering thinking. The fellows improve in machine learning, data science, communication skills, presentation skills, economic assessment, etc., to increase the importance of the company. As the real results of one of the case studies, the steam consumption of the petrochemical plant was reduced by 64 % and the yield of the production unit was increased. Eleven case studies were solved covering energy and resource efficiency, wastewater treatment, by-product recycling, new product development and other issues. As the final project results the mechanical specification of the equipment, new plant layout and economic efficiency were assessed.
