Modeling and optimization of process systems for unconventional technologies and feedstocks

In the present era, the petrochemical/chemical process industries must adapt to unconventional feedstocks and energy sources, in order to keep pace with increased competition, regulatory pressure, and changing markets. However, developing processes compatible with these changes requires deviating from traditional and accepted process design and operation paradigms. This dissertation addresses fundamental challenges related to this transition from three angles: incorporation of custom (and detailed) models into process design, integration of variable operation with process design, and optimization of transient process operations. The first part of the dissertation introduces a framework for modeling, simulation, and optimization of process flowsheets incorporating highly detailed physical models of important and complex process units, termed “multi-resolution flowsheets”. The framework relies on pseudo-transient continuation as a numerical method and allows for the robust optimization of large-scale process models. Several case studies demonstrate the method, including process flowsheets featuring both intensified (e.g., dividing-wall distillation column, multistream heat exchanger) and unconventional (e.g., quenched reactor, packed column for carbon capture) process units. Furthermore, these results reveal significant benefits of considering the added level of detail at the design stage. Finally, an avenue is presented to accelerate the convergence of the pseudo-transient method, which is especially important for the large-scale models considered. In the second part of the dissertation, the focus shifts to process design optimization for variable operation, or optimization under uncertainty. Here, I present a method for process design that considers the effect of uncertain physical parameters (assumed to follow continuous probability distributions), using a formulation that exploits the semi-infinite nature of dynamic optimization. I compare the method to traditional “scenario-based” approaches using both theoretical analyses and multiple case studies. In addition to demonstrating the effectiveness of the proposed method, these case studies also emphasize the importance of considering several practically relevant uncertainties during process design. The final part of the dissertation examines explicit consideration of process dynamics for operational optimization. First, I examine periodic (dynamically intensified) processes, which operate at a cyclic steady state. I present a pseudo-transient method for robust optimization of fully discretized dynamic process models, and I present an approach for implementing cyclic conditions based on their fundamental relation to material/energy recycle loops. Lastly, I propose a framework for optimal production scheduling in fast changing market situations. Towards this end, I show how data-driven dynamic models can represent the behavior of a set of scheduling-relevant (physical or latent) variables. A method is also given for executing scheduling calculations using these models, and the framework is demonstrated by considering the demand response operation of both simulated and real-world air separation units.Chemical Engineerin

When Deep Learning Meets Polyhedral Theory: A Survey

In the past decade, deep learning became the prevalent methodology for predictive modeling thanks to the remarkable accuracy of deep neural networks in tasks such as computer vision and natural language processing. Meanwhile, the structure of neural networks converged back to simpler representations based on piecewise constant and piecewise linear functions such as the Rectified Linear Unit (ReLU), which became the most commonly used type of activation function in neural networks. That made certain types of network structure \unicode{x2014}such as the typical fully-connected feedforward neural network\unicode{x2014} amenable to analysis through polyhedral theory and to the application of methodologies such as Linear Programming (LP) and Mixed-Integer Linear Programming (MILP) for a variety of purposes. In this paper, we survey the main topics emerging from this fast-paced area of work, which bring a fresh perspective to understanding neural networks in more detail as well as to applying linear optimization techniques to train, verify, and reduce the size of such networks

Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces

Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search, as they achieve good predictive performance with little or no manual tuning, naturally handle discrete feature spaces, and are relatively insensitive to outliers in the training data. Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function. To address both points simultaneously, we propose using the kernel interpretation of tree ensembles as a Gaussian Process prior to obtain model variance estimates, and we develop a compatible optimization formulation for the acquisition function. The latter further allows us to seamlessly integrate known constraints to improve sampling efficiency by considering domain-knowledge in engineering settings and modeling search space symmetries, e.g., hierarchical relationships in neural architecture search. Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.Comment: 27 pages, 9 figures, 4 table

Bridging the time and length scales of process systems with data

This report reviews the role of data as a “bridge” connecting the different time/length scales of chemical processes in mathematical modeling and multiscale, integrated decision making. This report further argues that this is a fitting role of “big data” in the chemical industry, an area that comprises complicated, yet deterministic, physical systems. Such systems can be described using physical and chemical laws that are generally well-understood. As such, data and their analyses are less likely to provide the unexpected and/or surprising insights that they have generated in other sectors (e.g., the transactional economy, social sciences). Nevertheless, historical operating data—which are often plentiful and available at little cost—can be converted to very useful information for multiscale mathematical modeling of chemical processes. Several examples of integration are provided, mapped on the continuum of time and length scales of chemical process systems. Existing research challenges and potential directions for future work are discussed.Chemical Engineerin

The integration of scheduling and control: Top-down vs. bottom-up

The flexible operation of continuous processes often requires the integration of scheduling and control. This can be achieved by top-down or bottom-up approaches. We compare the two paradigms in-silico using an air separation unit as a benchmark process. To demonstrate the top-down paradigm, we identify data-driven models of the closed-loop process dynamics based on a mechanistic model and use them in scheduling calculations that are performed offline. The resulting target trajectories are passed to a linear model predictive control (LMPC) system and implemented in the process. To demonstrate the bottom-up paradigm, we define an economic nonlinear model predictive control (eNMPC) scheme, which performs dynamic optimization using the full model in closed-loop to directly obtain the control variable profiles to be implemented in the process. We provide implementations of the process model equations as both a gPROMS and a Modelica model to encourage future comparison of approaches for flexible operation, process control, and/or handling disturbances. The performance, advantages, and disadvantages of the two strategies are analyzed using demand-response scenarios with varying levels of fluctuations in electricity prices, as well as considering the cases of known, instantaneous, and completely unknown load changes. The similarities and differences of the two approaches as relevant to flexible operation of continuous processes are discussed. Integrated scheduling and control leverages existing infrastructure and can be immediately applied to real operation tasks. Both operation strategies achieve successful process operation with remarkable economic improvements (up to 8%) compared to constant operation. eNMPC requires more computational resources, and is – at the moment – not implementable in real-time due to maximum optimization times exceeding the controller sampling time. However, eNMPC achieves up to 2.5 times higher operating cost savings compared to the top-down approach, owing in part to the more accurate modeling of key process dynamics

A Dynamic Optimization Approach to Probabilistic Process Design under Uncertainty

The process industry is moving toward rigorous flowsheet design optimization with modern algorithms. Optimization results are, however, influenced by uncertainty in the parameters of the mathematical models used in the optimization calculation. Parametric uncertainty is typically addressed using scenario-based approaches, whereby the process is optimized for a predetermined, finite set of scenarios representing the statistical properties of the parameters. This paper presents a novel approach for process design under uncertainty. The framework exploits the semi-infinite nature of sequential dynamic optimization, and is based on representing the uncertain parameters as continuous, time-varying disturbance variables acting on a (static) process model over a pseudo-time domain. The parameter uncertainty space is mapped by intersecting, continuous parameter trajectories instead of a limited set of discrete scenarios. We test the proposed strategy on two case studies: a dimethyl ether plant and the Williams-Otto process, demonstrating superior computational performance compared to scenario-based approaches