David C. Miller

Solving heat exchanger network synthesis problems with Tabu Search

Abstract This paper describes the implementation of a meta-heuristic optimization approach, Tabu Search (TS), for heat exchanger networks (HEN) synthesis and compares this approach to others presented in the literature. TS is a stochastic optimization approach that makes use of adaptive memory in the form of Tabu lists. Both recency- and frequency-based Tabu lists are used to provide short- and long-term knowledge of search history. TS is shown to locate the global optima with a high probability and low computation times, demonstrating the algorithm’s potential for solving a variety of other mixed integer nonlinear programming (MINLP) problems.

Tabu search algorithm for chemical process optimization

This paper presents a meta-heuristic optimization algorithm, Tabu Search (TS), and describes how it can be used to solve a wide variety of chemical engineering problems. Modifications to the original algorithm and constraint handling techniques are described and integrated to extend its applicability. All components of TS are described in detail. Initial values for each key parameter of TS are provided. In addition, guidelines for adjusting these parameters are provided to relieve a significant amount of time-consuming trial-and-error experiments that are typically required with stochastic optimization. Several small NLP and MINLP test cases and three small- to middle-scale chemical process synthesis problems demonstrate the feasibility and effectiveness of the techniques with recommended parameters.

Computer-aided molecular design using Tabu search

Abstract A detailed implementation of the Tabu search (TS) algorithm for computer-aided molecular design (CAMD) of transition metal catalysts is presented in this paper. Previous CAMD research has applied deterministic methods or genetic algorithms to the solution of the optimization problems which arise from the search for a molecule satisfying a set of property targets. In this work, properties are estimated using correlations based on connectivity indices, which allows the TS algorithm to use several novel operators to generate neighbors, such as swap and move, which would have no effect with a traditional group contribution-based approach. In addition, the formulation of the neighbor generation process guarantees that molecular valency and connectivity constraints are met, resulting in a complete molecular structure. Results on two case studies using TS are compared with a deterministic approach and show that TS is able to provide a list of good candidate molecules while using a much smaller amount of computation time.

Carbon Capture Simulation Initiative: A Case Study in Multiscale Modeling and New Challenges

Advanced multiscale modeling and simulation have the potential to dramatically reduce the time and cost to develop new carbon capture technologies. The Carbon Capture Simulation Initiative is a partnership among national laboratories, industry, and universities that is developing, demonstrating, and deploying a suite of such tools, including basic data submodels, steady-state and dynamic process models, process optimization and uncertainty quantification tools, an advanced dynamic process control framework, high-resolution filtered computational-fluid-dynamics (CFD) submodels, validated high-fidelity device-scale CFD models with quantified uncertainty, and a risk-analysis framework. These tools and models enable basic data submodels, including thermodynamics and kinetics, to be used within detailed process models to synthesize and optimize a process. The resulting process informs the development of process control systems and more detailed simulations of potential equipment to improve the design and reduce scale-up risk. Quantification and propagation of uncertainty across scales is an essential part of these tools and models.

A multi-objective analysis for the retrofit of a pulverized coal power plant with a CO2 capture and compression process

Abstract The long term sustainability of fossil energy systems depends on reducing their carbon footprint and freshwater consumption. Much of the United States is or will be experiencing water shortages in the near future. Since power generation accounts for about a third of all freshwater use, reducing freshwater requirements will be of increasing importance. In addition, recent reports indicate that adding a carbon capture system may double water consumption. Thus, when designing a carbon capture and compression system, it is important to consider not only the direct costs, but also the increased environmental burden associated with increased freshwater requirements. To address these interrelated sustainability issues, a modular framework for multi-objective analysis was developed and demonstrated by minimizing freshwater consumption and levelized cost of electricity for the retrofit of a hypothetical 550 MW subcritical pulverized coal power plant with an MEA-based carbon capture and compression system.

Environmentally-benign transition metal catalyst design using optimization techniques

Abstract Transition metal catalysts play a crucial role in many industrial applications, including the manufacture of lubricants, smoke suppressants, corrosion inhibitors and pigments. The development of novel catalysts is commonly performed using a trial-and-error approach which is costly and time-consuming. The application of computer-aided molecular design (CAMD) to this problem has the potential to greatly decrease the time and effort required to improve current catalytic materials in terms of their efficacy and biological effects. This work applies an optimization approach to redesign environmentally-benign homogeneous catalysts, specifically those which contain transition metal centers, to improve certain physical properties. Two main tasks must be achieved in order to perform the molecular design of a novel catalyst: biological and chemical properties must be estimated directly from the molecular structure, and the resulting optimization problem must be solved in a reasonable time. In this work, connectivity indices are used for the first time to predict the physical properties of a homogeneous catalyst. The existence of multiple oxidation states for transition metals requires a reformulation of the original equations for these indices. Once connectivity index descriptors have been defined for transition metal catalysts, structure–property correlations are then developed based on regression analysis using literature data for various properties of interest, including toxicity and electronegativity. These structure–property correlations are then used within an optimization framework to design novel homogeneous catalyst structures for use in a given application. The use of connectivity indices which define the topology of the molecule within the formulation guarantees that a complete molecular structure is obtained when the global optimum is found. In this work, second-order connectivity indices are used to obtain more information about steric features of the catalyst molecules, and non-linear correlations are employed to improve the accuracy of the property prediction equations. The structure–property correlations are then combined with linear structural feasibility constraints to form a mixed-integer non-linear program (MINLP), which when solved to optimality results in a catalyst molecule which most closely matches given property targets. To solve the resulting optimization problem, two methods are applied: Tabu search (a stochastic method), and outer approximation, a deterministic approach. For the outer approximation solution, a data structure is used which permits all equations except for the property prediction expressions to be written in linear forms. The computational efficiency of Tabu search is not strongly dependent on the existence of non-linear constraints, so for solution using this method, a non-linear form for the second-order connectivity index was chosen, which decreases the number of binary variables required. The solution methods are compared using three examples involving the design of environmentally-benign homogeneous catalysts containing molybdenum centers. Results show the efficacy of the formulation, and provide evidence that the Tabu search algorithm is more suitable for this type of molecular design algorithm than the commercially available deterministic approach.

A combined first-principles and data-driven approach to model building

Abstract We address a central theme of empirical model building: the incorporation of first-principles information in a data-driven model-building process. By enabling modelers to leverage all available information, regression models can be constructed using measured data along with theory-driven knowledge of response variable bounds, thermodynamic limitations, boundary conditions, and other aspects of system knowledge. We expand the inclusion of regression constraints beyond intra-parameter relationships to relationships between combinations of predictors and response variables. Since the functional form of these constraints is more intuitive, they can be used to reveal hidden relationships between regression parameters that are not directly available to the modeler. First, we describe classes of a priori modeling constraints. Next, we propose a semi-infinite programming approach for the incorporation of these novel constraints. Finally, we detail several application areas and provide extensive computational results.

Computational strategies for large-scale MILP transshipment models for heat exchanger network synthesis

Abstract Determining the minimum number of units is an important step in heat exchanger network synthesis (HENS). The MILP transshipment model ( Papoulias and Grossmann, 1983 ) and transportation model ( Cerda and Westerberg, 1983 ) were developed for this purpose. However, they are computationally expensive when solving for large-scale problems. Several approaches are studied in this paper to enable the fast solution of large-scale MILP transshipment models. Model reformulation techniques are developed for tighter formulations with reduced LP relaxation gaps. Solution strategies are also proposed for improving the efficiency of the branch and bound method. Both approaches aim at finding the exact global optimal solution with reduced solution times. Several approximation approaches are also developed for finding good approximate solutions in relatively short times. Case study results show that the MILP transshipment model can be solved for relatively large-scale problems in reasonable times by applying the approaches proposed in this paper.

Simultaneous process optimization and heat integration based on rigorous process simulations

Abstract This paper introduces a simultaneous process optimization and heat integration approach, which can be used directly with the rigorous models in process simulators. In this approach, the overall process is optimized utilizing external derivative-free optimizers, which interact directly with the process simulation. The heat integration subproblem is formulated as an LP model and solved simultaneously during optimization of the flowsheet to update the minimum utility and heat exchanger area targets. A piecewise linear approximation for the composite curve is applied to obtain more accurate heat integration results. This paper describes the application of this simultaneous approach for three cases: a recycle process, a separation process and a power plant with carbon capture. Case study results indicate that this simultaneous approach is relatively easy to implement and achieves higher profit and lower operating cost and, in the case of the power plant example, higher net efficiency than the sequential approach.

Advanced computational tools for optimization and uncertainty quantification of carbon capture processes

Advanced multi-scale modeling and simulation has the potential to dramatically reduce development time, resulting in considerable cost savings. The Carbon Capture Simulation Initiative (CCSI) is a partnership among national laboratories, industry and universities that is developing, demonstrating, and deploying a suite of multi-scale modeling and simulation tools. One significant computational tool is FOQUS, a Framework for Optimization and Quantification of Uncertainty and Sensitivity, which enables basic data submodels, including thermodynamics and kinetics, to be used within detailed process models to rapidly synthesize and optimize a process and determine the level of uncertainty associated with the resulting process. The overall approach of CCSI is described with a more detailed discussion of FOQUS and its application to carbon capture systems.