Introduction Companies must design and operate chemical processes effectively and efficiently so they may survive in today’s highly competitive world. Providing the methods, tools and people that allow industry to meet its needs by tying science to engineering is a compelling aspect of Process Systems Engineering (PSE). Despite the importance of the PSE, the scope and research of this area are often not well understood. One of the major reasons is that chemical engineering has evolved over the past five decades from being an engineering discipline rooted in the concept of unit operations to one based on engineering science and mathematics, and most recently to one with increasing ties to the natural sciences. This very significant change in emphasis has created a gap between the science-based and the systems-based research in chemical engineering. We argue here that this gap might be closed in two ways: first, by broadening the definition of Process Systems Engineering through the introduction of the concept of the “chemical supply chain,” and second, by gaining a better appreciation of the intellectual research challenges in this area. We address this last issue by discussing the nature and major accomplishments of the PSE area and outlining emerging research directions.

Research challenges in process systems engineering

To achieve goals for climate and economic growth, “negative emissions technologies” (NETs) that remove and sequester carbon dioxide from the air will need to play a significant role in mitigating climate change. Unlike carbon capture and storage technologies that remove carbon dioxide emissions directly from large point sources such as coal power plants, NETs remove carbon dioxide directly from the atmosphere or enhance natural carbon sinks. Storing the carbon dioxide from NETs has the same impact on the atmosphere and climate as simultaneously preventing an equal amount of carbon dioxide from being emitted. Recent analyses found that deploying NETs may be less expensive and less disruptive than reducing some emissions, such as a substantial portion of agricultural and land-use emissions and some transportation emissions. In 2015, the National Academies published Climate Intervention: Carbon Dioxide Removal and Reliable Sequestration, which described and initially assessed NETs and sequestration technologies. This report acknowledged the relative paucity of research on NETs and recommended development of a research agenda that covers all aspects of NETs from fundamental science to full-scale deployment. To address this need, Negative Emissions Technologies and Reliable Sequestration: A Research Agenda assesses the benefits, risks, and “sustainable scale potential” for NETs and sequestration. This report also defines the essential components of a research and development program, including its estimated costs and potential impact.

Negative Emissions Technologies and Reliable Sequestration: A Research Agenda

Optimization strategies for flexible chemical processes

This work presents a review of the main deterministic mixed-integer nonlinear programming (MINLP) solution methods for problems with convex and nonconvex functions. An overview for deriving MINLP formulations through generalized disjunctive programming (GDP), which is an alternative higher-level representation of MINLP problems, is also presented. A review of solution methods for GDP problems is provided. Some relevant applications of MINLP and GDP in process systems engineering are described in this work.

Review of Mixed-Integer Nonlinear and Generalized Disjunctive Programming Methods

Process systems engineering: From Solvay to modern bio- and nanotechnology.: A history of development, successes and prospects for the future

Strategy of Process Engineering

Estrategia en ingeniería de procesos

A common method of accounting for uncertainty in an engineering project is to assign a minimum acceptable rate of return on capital investments. This minimum rate of return is assigned in a rather arbitrary way according to the expected risk in the project. In this paper we introduce the concepts of statistical decision theory and show how the minimum rate of return can be estimated in terms of uncertainty in sales income, project life, investment, manufacturing costs, etc.



Il est pratique courante de tenir compte des incertitudes dans un projet de genie en appliquant un taux minimum acceptable de revenu de placement. Ce taux minimum est une fonction arbitraire du risque de l'entreprise. Nous presentons les concepts de la theorie de decision statistique et demontrons comment ce taux minimum de revenu de placement est evalue en fonction de l'incertitude dans les ventes, le revenu net, le placement, la vie de l'entreprise, les coǔts de production, etc.

Charles C. Watson

Papers

Strategy of Process Engineering

Estrategia en ingeniería de procesos

On corrective factors for project uncertainty