Integrated design and control of chemical processes – Part I: Revision and classification

Uncertainty in chemical process systems engineering: a critical review

Uncertainty or error occurs as a result of a lack or misuse of knowledge about specific topics or situations. In this review, we recall the differences between error and uncertainty briefly, first, and then their probable sources. Then, their identifications and management in chemical process design, optimization, control, and fault detection and diagnosis are illustrated. Furthermore, because of the large amount of information that can be obtained in modern plants, accurate analysis and evaluation of those pieces of information have undeniable effects on the uncertainty in the system. Moreover, the origins of uncertainty and error in simulation and modeling are also presented. We show that in a multidisciplinary modeling approach, every single step can be a potential source of uncertainty, which can merge into each other and generate unreliable results. In addition, some uncertainty analysis and evaluation methods are briefly presented. Finally, guidelines for future research are proposed based on existing research gaps, which we believe will pave the way to innovative process designs based on more reliable, efficient, and feasible optimum planning.

Integrated Design and Control of Various Hydrogen Production Flowsheet Configurations via Membrane Based Methane Steam Reforming

This work focuses on the development and implementation of an integrated process design and control framework for a membrane-based hydrogen production system based on low temperature methane steam reforming. Several alternative flowsheet configurations consisted of either integrated membrane reactor modules or successive reactor and membrane separation modules are designed and assessed by considering economic and controller dynamic performance criteria simultaneously. The design problem is expressed as a non-linear dynamic optimization problem incorporating a nonlinear dynamic model for the process system and a linear model predictive controller aiming to maintain the process targets despite the effect of disturbances. The large dimensionality of the disturbance space is effectively addressed by focusing on disturbances along the direction that causes the maximum process variability revealed by the analysis of local sensitivity information for the process system. Design results from a multi-objective optimization study, where only the annualized equipment and operational costs are minimized, are used as reference case in order to evaluate the proposed design framework. Optimization results demonstrate the controller’s ability to track the imposed setpoint changes and alleviate the effects of multiple simultaneous disturbances. Also, significant economic improvements are observed by the implementation of the integrated design and control framework compared to the traditional design methodology, where process and controller design are performed sequentially.