This contribution describes a novel process systems engineering framework that couples advanced control with sustainability evaluation for the optimization of process operations to minimize environmental impacts associated with products, materials and energy. The implemented control strategy combines a biologically-inspired method with optimal control concepts for finding more sustainable operating trajectories. The sustainability assessment of process operating points is carried out by using the U.S. EPA’s Gauging Reaction Effectiveness for the ENvironmental Sustainability of Chemistries with a multi-Objective Process Evaluator (GREENSCOPE) tool that provides scores for the selected indicators in the economic, material efficiency, environmental and energy areas. The indicator scores describe process performance on a sustainability measurement scale, effectively determining which operating point is more sustainable if there are more than several steady states for one specific product manufacturing. Through comparisons between a representative benchmark and the optimal steady states obtained through the implementation of the proposed controller, a systematic decision can be made in terms of whether the implementation of the controller is moving the process towards a more sustainable operation. The effectiveness of the proposed framework is illustrated through a case study of a continuous fermentation process for fuel production, whose material and energy time variation models are characterized by multiple steady states and oscillatory conditions.

Our societal needs for greener, economically viable products and processes have grown given the adverse environmental impact and unsustainable development caused by human activities, including chemical releases, exposure, and impacts. To make chemical processes safer and more sustainable, a novel sustainability-oriented control strategy is developed in this work. This strategy enables the incorporation of online sustainability assessment and process control with sustainability constraints into chemical process operations. Specifically, U.S. Environmental Protection Agency (EPA)’s GREENSCOPE (Gauging Reaction Effectiveness for the ENvironmental Sustainability of Chemistries with a multi-Objective Process Evaluator) tool is used for sustainability assessment and environmental release minimization of chemical processes. The multivariable GREENSCOPE indicators in real time can be represented using a novel visualization method with dynamic radar plots. The analysis of the process dynamic behavior in terms of sustainability performance provides means of defining sustainability constraints for the control strategy to improve process sustainability aspects with lower scores. For the control task, Biologically Inspired Optimal Control Strategy (BIO-CS) is implemented with sustainability constraints so that the control actions can be calculated considering the sustainability performance. This work leads to a significant step forward towards augmenting the capability of process control to meet future demands on multiple control objectives (e.g., economic, environmental, and safety related). The effectiveness of the proposed framework is illustrated via two case studies associated with a fermentation system. The results show that the proposed control strategy can effectively drive the system to the desired setpoints while meeting a preset sustainability constraint and improving the transient sustainability performance by up to 16.86% in terms of selected GREENSCOPE indicators.

Industry, government, and society have begun to shift from economic stand-alone focus to the inclusion of sustainability in the decision-making process. This shift can be attributed to the growing environmental and social awareness that makes consumers and stakeholders not only care about product quality and cost, but also products and processes that minimize the environmental impact and conserve natural resources. As a result, some progress has been made in recognizing and understanding the challenges of sustainable development, which helps evaluating the sustainability performance of a specific process/product. In particular, sustainability assessment tools and methodologies have been developed to help the future engineer and scientist with designing and optimizing the chemical processes in terms of sustainability. However, an efficient framework for the integration of advanced process control with sustainability assessment technologies is still missing. In this dissertation, a novel process systems framework for integrating sustainability assessment, optimization, and advanced control is developed to simultaneously optimize and control chemical process systems at the optimal operating points considering efficiency, environmental, economic and energy aspects. This proposed framework bridges gaps in the literature by addressing the following challenges: 1) assessment of sustainability performance at steady-state and dynamic operations; 2) integration of sustainability indicators into a process control framework. Specifically, the proposed framework contains three main components: an integrated sustainability assessment tool, a multi-objective optimization formulation, and an advanced control strategy. The integrated sustainability assessment tool is developed using a user-friendly automation interface that enables the communication between process simulation, pollution control units (PCUs), life cycle inventory (LCI) generation, and the U.S. EPA’s GREENSCOPE (Gauging Reaction Effectiveness for the ENvironmental Sustainability of Chemistries with a Multi-Objective Process Evaluator) tool. With the sustainability indicators for chemical processes, a multi-objective optimization problem is formulated to identify the Pareto set of most sustainable operating conditions using a genetic algorithm for the decision-making step. To better balance the trade-off between economic and environmental aspects, a sustainable control strategy is implemented to drive the process to the chosen Pareto optimal solution while meeting pre-defined sustainability constraints. For the control implementation, a novel visualization method is developed to visualize the dynamic multidimensional sustainability indicators during transient. To illustrate the effectiveness of the developed framework, two chemical processes are addressed: i) a continuous fermentation process for bio-ethanol production, for which the implemented sustainable process control scheme can improve the performance in terms of sustainability by 9.65% ~ 16.86% for different operating conditions;