Richard C. Pattison

Pseudo-transient models for multiscale, multiresolution simulation and optimization of intensified reaction/separation/recycle processes: Framework and a dimethyl ether production case study

Abstract Including detailed models of processing equipment in the process flowsheet model is required in the case of “non-standard” unit operations, including, e.g., intensified equipment and unconventional reactor designs. We provide a unified multiscale framework for including such models in equation-oriented process flowsheet modeling, simulation and optimization. Relying on the reaction/separation/recycle process prototype, we propose a multiresolution paradigm, whereby detailed, distributed-parameter representations of reacting systems and rigorous models of (intensified) separation units are embedded in process flowsheet models. We develop an equation-oriented modeling approach based on a pseudo-transient reformulation of the balance equations, enabling the reliable and robust simulation of the process flowsheet. We also describe a companion design optimization routine. We illustrate these concepts with an extensive case study on dimethyl ether production using an intensified process featuring a dividing-wall distillation column and an adiabatic packed bed reactor with intermediate quenching.

Robust autothermal microchannel reactors

Abstract Autothermal microchannel reactors are intensified process units that bring significant energy efficiency benefits over their conventional counterparts. Efficiency gains are obtained, however, at the cost of operational challenges. These stem from the loss of control handles that is inherent to combining several unit operations in a single physical device. In this paper, we investigate the impact of two recently proposed reactor design concepts (a segmented catalyst macromorphology and an embedded layer of phase change material) aimed at improving the steady state energy distribution and, respectively, preventing the advent of hotspots during transient operation, on reactor dynamics and control. Using an autothermal microchannel reactor coupling steam methane reforming with methane catalytic combustion as a prototype system, we demonstrate through rigorous simulations that these design innovations have a synergistic effect, resulting in superior steady-state performance and excellent disturbance rejection ability.

Optimal design of air separation plants with variable electricity pricing

Abstract Air separation processes are significant electricity consumers. Operating air separation units (ASUs) at variable capacity can be economically beneficial (in time varying electricity price scenarios) and can serve as a means for mitigating grid load during peak times. Process design should thus account for such operational fluctuations. In this paper, we study the optimal design of ASUs assumed to be operating at variable capacity under variable electricity price. We introduce a novel pseudo-transient equation-oriented framework for process modelling, and use the developed models together with a time relaxation-based optimization algorithm to optimize the design of the plant subject to a time-of-day price scenario.

Handling input dynamics in integrated scheduling and control

Coordinating production scheduling decisions with the process control system requires considering the evolution of the process over multiple time scales and at multiple levels of detail. From a mathematical perspective, this requires dealing with process models that are large-scale, ill-conditioned and involve both continuous and discrete variables (the former related to physical states, while the latter reflect production management decisions). In this paper, we introduce a novel methodology for time scale-bridging between production scheduling and process control. We use process operating data to obtain low-order models of the closed-loop behavior of the process, which are then incorporated in the production scheduling framework. The theoretical developments are accompanied by an illustrative case study on a methyl methacrylate process, showing excellent economic results and significantly improved computational performance.

An efficient MILP framework for integrating nonlinear process dynamics and control in optimal production scheduling calculations

Abstract The emphasis currently placed on enterprise-wide decision making and optimization has led to an increased need for methods of integrating nonlinear process dynamics and control information in scheduling calculations. The inevitable high dimensionality and nonlinearity of first-principles dynamic process models makes incorporating them in scheduling calculations challenging. In this work, we describe a general framework for deriving data-driven surrogate models of the closed-loop process dynamics. Focusing on Hammerstein–Wiener and finite step response (FSR) model forms, we show that these models can be (exactly) linearized and embedded in production scheduling calculations. The resulting scheduling problems are mixed-integer linear programs with a special structure, which we exploit in a novel and efficient solution strategy. A polymerization reactor case study is utilized to demonstrate the merits of this method. Our framework compares favorably to existing approaches that embed dynamics in scheduling calculations, showing considerable reductions in computational effort.

A survey of optimal process design capabilities and practices in the chemical and petrochemical industries

Abstract To examine industrial capabilities and practices in using process design optimization software tools, we conducted a series of over one hundred interviews with practitioners and industry experts in optimal process design, focusing on current techniques, workflows, and challenges. In this article, we analyze the findings of these interviews, providing a perspective into the status of optimal process design in the petrochemical and chemical industries. We first present the findings categorized by company type and personnel function, followed by industry-specific insights.

A Simulation-based Optimization Framework for Integrating Scheduling and Model Predictive Control, and its Application to Air Separation Units

Abstract The integration of dynamic process models in scheduling calculations has recently received significant attention as a mean to improve operational performance in increasingly dynamic markets. In this work, we propose a novel framework for the integration of scheduling and model predictive control (MPC), which is applicable to industrial size problems involving fast changing market conditions. The framework consists on identifying scheduling-relevant process variables, building low-order dynamic models to capture their evolution, and integrating scheduling and MPC by, (i) solving a simulation-optimization problem to define the optimal schedule and, (ii) tracking the schedule in closed-loop using the MPC controller. The efficacy of the framework is demonstrated via a case study that considers an air separation unit operating under real-time electricity pricing. The study shows that significant cost reductions can be achieved with reasonable computational times.

Latent storage-enhanced distributed temperature control in hydrogen microreactors

Microchannel catalytic plate reactors are a promising route for converting methane from geographically distributed sources (e.g., shale gas deposits) to hydrogen or liquid transportation fuels. Their capacity is easily scalable by increasing the number of units and thus well suited to distributed production needs. However, miniaturization inevitably reduces the number of available actuators and sensors, and the control of these inherently distributed systems presents challenges. In the present paper, we concentrate on autothermal microchannel reactors producing hydrogen via methane-steam reforming, and introduce a novel temperature control strategy based on the use of a layer of phase-change material (PCM) confined between the reactor plates. The PCM layer, which mitigates temperature excursions through melting-solidification occurring due to fluctuations in hydrogen production rate, acts as the distributed tier of a hierarchical control structure. The supervisory layer consists of a model-based feedforward controller. We also introduce a novel stochastic optimization method for selecting the PCM layer thickness (i.e., for distributed controller “tuning”). The proposed approach is tested in simulations carried out on a detailed 2D reactor model, showing excellent disturbance rejection performance.

Temperature Control Applications of the Phase Transformations of Materials

Abstract The constant-temperature heat accumulation and release cycles associated with the phase transformations of materials constitute a natural mechanism for temperature regulation. In this paper, we review the basic tenets of the dynamics of the melting-solidification cycles of phase-change materials (PCMs) and a recently developed systems perspective on the benefits and challenges of PCM-based temperature control. We also describe a recently developed optimization-based approach for selecting the geometry of PCM heat sinks and present its application in two situations of practical interest, controlling the temperature of a microchip and preventing temperature runaway in a microchannel reactor.

A superstructure-based design of experiments framework for simultaneous domain-restricted model identification and parameter estimation

Abstract We present a novel design of experiments (DOE) approach to incorporate model identification into optimal experimental designs based on a postulated model superstructure and an associated relaxation strategy. We show that an adaptive online design of experiments allows for the accurate estimation of the parameters of a domain-restricted model, as well as the model structure and domain on which that model is valid. We further show that previous attempts at combining model identification and parameter estimation are a special case of this framework (when the objective function is formulated in terms of the trace of the Fisher information matrix), and thus the proposed formulation provides the option to use alternate or more complex objective functions. The efficacy of the proposed framework is shown through two case studies: a batch reactor with Arrhenius-type reactions and a carbon dioxide adsorption system.