Advances in Green Process Systems Engineering

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Environmental and Green Processes".

Deadline for manuscript submissions: 15 April 2026 | Viewed by 545

Special Issue Editors

State Key Laboratory of Industrial Control Technology, Institute of Industrial Intelligence and Systems Engineering, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Interests: process systems engineering; process simulation and optimization; process design; uncertainty analysis; quality control; digital twin; smart manufacturing
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Guest Editor
School of Chemistry and Chemical Engineering, Institute for Sustainability, University of Surrey, Guildford, Surrey GU2 7XH, UK
Interests: process systems engineering; bioenergy; artificial intelligence; energy systems; optimization; process control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Process industries are an essential foundation of the economy in many countries, involving multiple fields such as petrochemical engineering, metallurgical engineering, materials, energy, food processing, pharmaceuticals, and environmental engineering. The rapid evolution of process technology is leading to an ever-growing demand for resources and an increasingly deteriorating ecological environment. These challenges in process industries in terms of environmental protection and sustainable development contribute to the emergence and growth of green process systems engineering (PSE).

Green PSE is an emerging interdisciplinary field that integrates PSE techniques with green sustainability principles. By leveraging advanced modeling, design, optimization, and integration techniques, green PSE aims to increase resource efficiency, reduce energy consumption, minimize pollution emissions, and enhance recycling capabilities. The development of green PSE technology not only requires innovation and efficiency in raw materials, solvents, catalysts, and unit equipment but also needs to achieve a multi-objective optimization of the economy, environment, safety, and sustainability from a systematic perspective. Key focus areas include renewable energy development and integration, industrial intelligence, digital twins, multi-scale simulation, carbon capture and utilization, the circular economy, AI-driven process optimization, etc. Green PSE can provide a systematic framework for developing cleaner, smarter, and more sustainable manufacturing solutions, aligning economic growth with ecological responsibility.

This Special Issue will showcase state-of-the-art advances in PSE technologies and their applications in promoting green and sustainable developments across chemical, energy, material, and pharmaceutical industries.

We seek to highlight a broad range of topics, including but not limited to the following:

  • Advanced process simulation and optimization technologies, with a special focus on multi-scale modeling, quality control, and production lifecycle management.
  • Exploration and development of renewable energy and materials.
  • Process technologies for high energy efficiency and low emissions.
  • Implementations of intelligent industry, emphasizing the importance of industrial software, industrial large model, and digital twin techniques.
  • Production safety techniques, such as production flexibility and system reliability.
  • Challenges and potential research directions in environmental protection and sustainable development.

Dr. Fei Zhao
Dr. Michael Short
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • green chemical engineering
  • renewable energy
  • smart manufacturing
  • process simulation and optimization
  • process intensification
  • multi-scale simulation
  • carbon capture and utilization
  • safety and environmental protection

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Published Papers (1 paper)

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Research

19 pages, 12509 KB  
Article
Trajectory Tracking Control of Hydraulic Flexible Manipulators Based on Adaptive Robust Model Predictive Control
by Jinwei Jiang, Li Wu and Zhen Sui
Processes 2025, 13(11), 3638; https://doi.org/10.3390/pr13113638 - 10 Nov 2025
Viewed by 417
Abstract
Aiming at the trajectory tracking control problem caused by the coupling of strong nonlinearity, parameter uncertainty and unknown disturbances in rigid robotic arms, this paper proposes an adaptive robust model predictive control (APRMPC) scheme. This study aims to fill the gap in the [...] Read more.
Aiming at the trajectory tracking control problem caused by the coupling of strong nonlinearity, parameter uncertainty and unknown disturbances in rigid robotic arms, this paper proposes an adaptive robust model predictive control (APRMPC) scheme. This study aims to fill the gap in the existing literature by proposing a dedicated control framework capable of simultaneously and effectively handling parameter uncertainty, unmodeled dynamics, and external disturbances, while ensuring constraint satisfaction. Firstly, a dynamic model of a three-degree-of-freedom robotic arm was established based on the Lagrange equation; secondly, this paper designs a deep integration mechanism of adaptive law and robust predictive control: by designing a parameter adaptive algorithm to estimate the system uncertainty online and feedforward compensate it to the predictive model, the impact of model mismatch is significantly reduced; meanwhile, for the estimated residuals and unknown disturbances, feedback gain was introduced and the control input was designed based on the robust invariant set theory, achieving unified parameter identification, disturbance suppression and rolling optimization within a single framework. This paper strictly proves the feasibility and stability of the control scheme. Finally, the simulation experiments based on MATLAB show that, compared with the traditional MPC and PID methods, the APRMPC algorithm can achieve higher accuracy and stronger robustness in trajectory tracking under various working conditions, effectively resolving the inherent contradiction between the weak robustness of the traditional MPC and the large buffering of sliding mode control, and verifying the value of the proposed scheme in filling the gap in related literature. Full article
(This article belongs to the Special Issue Advances in Green Process Systems Engineering)
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