APPLICATION OF PROCESS SYSTEMS ENGINEERING TOOLS AND METHODS TO FERMENTATION-BASED BIOREFINERIES

Biofuels produced from lignocellulosic biomass via the fermentation platform are sustainable energy alternatives to fossil fuels. Process Systems Engineering (PSE) uses computer-based tools and methods to design, simulate and optimize processes. Application of PSE tools to the design of economic biorefinery processes requires the development of simulation approaches that can be integrated with existing, mature PSE tools used to optimize traditional refineries, such as Aspen Plus. Current unit operation models lack the ability to describe unsteady state fermentation processes, link unsteady state fermentation with in situ separations, and optimize these processes for competing factors (e.g., yield and productivity). This work applies a novel architecture of commercial PSE tools, Aspen Plus and MATLAB, to develop techniques to simulate time-dependent fermentation without and with in situ separations for process design, analyses and optimization of the operating conditions. Traditional batch fermentation simulations with in situ separations decouple these interdependent steps in a separate “steady state” reactor followed by an equilibrium separation of the final fermentation broth. A typical mechanistic system of ordinary differential equations (ODEs) describing a batch fermentation does not fit the standard built-in power law reaction kinetics model in Aspen Plus. To circumvent this challenge, a novel platform that links the batch reactor to a FORTRAN user kinetics subroutine (incorporates the ODEs) combined with component substitution (to simulate non-databank components) is utilized to simulate an unsteady state batch and in situ gas stripping process. The resulting model system predicts the product profile to be sensitive to the gas flow rate unlike previous “steady state” simulations. This demonstrates the importance of linking a time-dependent fermentation model to the fermentation environment for the design and analyses of fermentation processes. A novel platform linking the genetic algorithm multi-objective and single-objective optimizations in MATLAB to the unsteady state batch fermentation simulation in Aspen Plus through a component object module communication platform is utilized to optimize the operating conditions of a typical batch fermentation process. Two major contributions are: prior concentration of sugars from a typical lignocellulosic hydrolysate may be needed and with a higher initial sugar concentration, the fermentation process must be integrated with an in situ separation process to optimize the performance of fermentation processes. With this framework, fermentation experimentalists can use the full suite of PSE tools and methods to integrate biorefineries and refineries and as a decision-support tool to guide the design, analyses and optimization of fermentation-based biorefineries

Optimization of Process Flowsheets through Metaheuristic Techniques

This book presents a multi-objective optimization framework for optimizing chemical processes. The proposed framework implements a link between process simulators and metaheuristic techniques. The proposed approach is general, and there can be used any process simulator and any metaheuristic technique. This book shows how to implement links between different process simulators such as Aspen Plus®, HYSYS®, SuperPro Designer®, and others, linked to metaheuristic techniques implemented in Matlab®, Excel®, C++, or other programs. This way, the proposed framework allows optimizing any process flowsheet implemented in the process simulator and using the metaheuristic technique, and this way the numerical complications through the optimization process can be eliminated. Furthermore, the proposed framework allows using the thermodynamic, design, and constitutive equations implemented in the process simulator to implement any process

Integration of modular process simulators under the Generalized Disjunctive Programming framework for the structural flowsheet optimization

The optimization of chemical processes where the flowsheet topology is not kept fixed is a challenging discrete-continuous optimization problem. Usually, this task has been performed through equation based models. This approach presents several problems, as tedious and complicated component properties estimation or the handling of huge problems (with thousands of equations and variables). We propose a GDP approach as an alternative to the MINLP models coupled with a flowsheet program. The novelty of this approach relies on using a commercial modular process simulator where the superstructure is drawn directly on the graphical use interface of the simulator. This methodology takes advantage of modular process simulators (specially tailored numerical methods, reliability, and robustness) and the flexibility of the GDP formulation for the modeling and solution. The optimization tool proposed is successfully applied to the synthesis of a methanol plant where different alternatives are available for the streams, equipment and process conditions.Spanish Ministry of Science and Innovation (CTQ2012-37039-C02-02)

Integration of different models in the design of chemical processes: Application to the design of a power plant

With advances in the synthesis and design of chemical processes there is an increasing need for more complex mathematical models with which to screen the alternatives that constitute accurate and reliable process models. Despite the wide availability of sophisticated tools for simulation, optimization and synthesis of chemical processes, the user is frequently interested in using the ‘best available model’. However, in practice, these models are usually little more than a black box with a rigid input–output structure. In this paper we propose to tackle all these models using generalized disjunctive programming to capture the numerical characteristics of each model (in equation form, modular, noisy, etc.) and to deal with each of them according to their individual characteristics. The result is a hybrid modular–equation based approach that allows synthesizing complex processes using different models in a robust and reliable way. The capabilities of the proposed approach are discussed with a case study: the design of a utility system power plant that has been decomposed into its constitutive elements, each treated differently numerically. And finally, numerical results and conclusions are presented.Spanish Ministry of Science and Innovation (CTQ2012-37039-C02-02)

A Comprehensive Optimization Framework for Designing Sustainable Renewable Energy Production Systems

As the world has recognized the importance of diversifying its energy resource portfolio away from fossil resources and more towards renewable resources such as biomass, there arises a need for developing strategies which can design renewable sustainable value chains that can be scaled up efficiently and provide tangible net environmental benefits from energy utilization. The objective of this research is to develop and implement a novel decision-making framework for the optimal design of renewable energy systems. The proposed optimization framework is based on a distributed, systematic approach which is composed of different layers including systems-based strategic optimization, detailed mechanistic modeling and operational level optimization. In the strategic optimization the model is represented by equations which describe physical flows of materials across the system nodes and financial flows that result from the system design and material movements. Market uncertainty is also incorporated into the model through stochastic programming. The output of the model includes optimal design of production capacity of the plant for the planning horizon by maximizing the net present value (NPV). The second stage consists of three main steps including simulation of the process in the simulation software, identification of critical sources of uncertainties through global sensitivity analysis, and employing stochastic optimization methodologies to optimize the operating condition of the plant under uncertainty. To exemplify the efficacy of the proposed framework a hypothetical lignocellulosic biorefinery based on sugar conversion platform that converts biomass to value-added biofuels and biobased chemicals is utilized as a case study. Furthermore, alternative technology options and possible process integrations in each section of the plant are analysed by exploiting the advantages of process simulation and the novel hybrid optimization framework. In conjunction with the simulation and optimization studies, the proposed framework develops quantitative metrics to associate economic values with technical barriers. The outcome of this work is a new distributed decision support framework which is intended to help economic development agencies, as well as policy makers in the renewable energy enterprises

Hybrid simulation-equation based synthesis of chemical processes

A challenging problem in the synthesis and design of chemical processes consists of dealing with hybrid models involving process simulators and explicit constraints. Some unit operations in modular process simulators are slightly noisy or require large CPU times to converge. In this work, this problem is addressed by combining process simulators and surrogate models. We have replaced some unit operations, which cannot be used directly with a gradient-based optimization, by surrogate models based on Kriging interpolation. To increase the robustness of the resulting optimization model, we perform a degree of freedom analysis and aggregate (or disaggregate) parts of the model to reduce the number of independent variables of the Kriging surrogate models (KSMs). Thus, the final model is composed of KSMs, unit operations (maintained in the process simulator) and also explicit equations. The optimization of the well-known vinyl chloride monomer (VCM) production process is performed to test the proposed approach. The effect of the heat integration is also studied. In addition, the economic feasibility of the optimized process is calculated assuming uncertainty in raw material and product prices.The authors gratefully acknowledge the financial support by the Ministry of Economy and Competitiveness from Spain, under the project CTQ2016-77968-C3-02-P (AEI/FEDER, UE), and Call 2013 National Sub-Program for Training, Grants for pre-doctoral contracts for doctoral training (BES-2013-064791)

Modeling, Analysis and Optimization of the Gas-Phase Methanol Synthesis Process

Methanol synthesis has been the subject of many improvements over the last decades since it became more cost effective and scalable than earlier high pressure technology. The synthesis of methanol from syngas has conventionally been carried out in adiabatic quench-type reactor in the gas phase where the only way to moderate the temperature is to inject shots of syngas at various position of the rector. However, because of the highly exothermic behavior of methanol synthesis reactions, the dissipation of heat has been a bottle-neck in the reactor design, and reactor configurations have a tendency to be complicated. This dissertation is divided into three parts presents a mathematical model of double-tube methanol reactor which was developed through cooperation between Mitsubishi Heavy Industries (MHI) and Mitsubishi Gas Company (MGC), methanol synthesis process flowsheet was developed and fully integrated with the Genetic Algorithms that generated a set of optimal operating conditions with respect to upper and lower limits and several constraints, and a dynamic optimization approaches to derive the ideal operating conditions for a Lurgi type reactor in the presence of catalyst deactivation. The first part of dissertation concentrates on the Mitsubishi Methanol “superconverter” which has a design capability to efficiently remove the heat generated by the exothermic reactions in methanol synthesis and improves methanol production by at least 3% more than the conventional single-tube converter. This converter is operated under milder conditions, especially at the end of the reactor, allows the catalyst to last for a longer period. This leads to process intensification and allows for the use of a compact distillation step. In addition, this new design has the advantage of preheating the feed gas to the reactor by having the inner tubes replace the feed gas preheater. The predicted methanol concentration and temperature profiles indicate that an increase in temperature is accompanied with a reduction in the methanol equilibrium concentration and hence limiting the profitability in the industrial plant. The use of a double-tube reactor is shown to be able to overcome this limitation. The novelty lies in a process modification which employs an inner tube that is disposed in the reactor and then the catalyst is charged into a circular space surrounded by the reaction tube on one side and inner tube on the other side. Simulation studies show that this design allows the temperature to increase gradually and, hence, delays the equilibrium to be reached to the end of the reactor. In other words, more methanol is produced and less byproducts. The second part of the dissertation concentrates on a multi-objective optimization applied for the operating conditions of the methanol synthesis loop via a multi-stage fixed bed adiabatic reactor system with an additional inter-stage CO2 quenching stream to maximize methanol production while reducing CO2 emissions. The model prediction for the methanol synthesis loop at steady state showed good agreement against data from an existing commercial plant. Later, the process flowsheet was developed and fully integrated with the Genetic Algorithms Toolbox that generated a set of optimal operating conditions with respect to limits and linear constraints. The results showed methanol production was improved by injecting shots of carbon dioxide recovered from the reformer at various reactor locations. The third part of the dissertation concentrates on a dynamic optimization approach derived the ideal operating conditions for a Lurgi-type methanol reactor in the presence of catalyst deactivation are proposed to determine the optimal use of recycle ratio of CO2 and shell coolant temperature without violating any process constraints. This study proposes a new approach based on a hybrid algorithm combining genetic algorithm (GA) and generalized pattern search (GPS) derivative-free methodologies to provide a sufficiently good solution to this dynamic optimization problem. The hybrid GA-GPS algorithm has the advantage of sequentially combining GA and GPS logics; while GA, as the most popular evolutionary algorithm, effectively explore the landscape of the fitness function and identify promising areas of the search space, GPS efficiently search existing basins in order to find an approximately optimal solution. The simulation results showed that implementing the shell temperature trajectory derived by the proposed approach with 5% recycle ratio of CO2 increased the production of methanol by approximately 2.5% compared to the existing operating conditions

Large scale optimization of a sour water stripping plant using surrogate models

In this work, we propose a new methodology for the large scale optimization and process integration of complex chemical processes that have been simulated using modular chemical process simulators. Units with significant numerical noise or large CPU times are substituted by surrogate models based on Kriging interpolation. Using a degree of freedom analysis, some of those units can be aggregated into a single unit to reduce the complexity of the resulting model. As a result, we solve a hybrid simulation-optimization model formed by units in the original flowsheet, Kriging models, and explicit equations. We present a case study of the optimization of a sour water stripping plant in which we simultaneously consider economics, heat integration and environmental impact using the ReCiPe indicator, which incorporates the recent advances made in Life Cycle Assessment (LCA). The optimization strategy guarantees the convergence to a local optimum inside the tolerance of the numerical noise.The authors wish to acknowledge the financial support by the Ministry of Economy and Competitiveness of Spain, under the project CTQ2012-37039-C02-02

Economic and environmental impacts of the energy source for the utility production system in the HDA process

The well-known benchmark process for hydrodealkylation of toluene (HDA) to produce benzene is revisited in a multi-objective approach for identifying environmentally friendly and cost-effective operation solutions. The paper begins with the presentation of the numerical tools used in this work, i.e., a multi-objective genetic algorithm and a Multiple Choice Decision Making procedure. Then, two studies related to the energy source involved in the utility production system (UPS), either fuel oil or natural gas, of the HDA process are carried out. In each case, a multi-objective optimization problem based on the minimization of the total annual cost of the process and of five environmental burdens, that are Global Warming Potential, Acidification Potential, Photochemical Ozone Creation Potential, Human Toxicity Potential and Eutrophication Potential, is solved and the best solution is identified by use of Multiple Choice Decision Making procedures. An assessment of the respective contribution of the HDA process and the UPS towards environmental impacts on the one hand, and of the environmental impacts generated by the main equipment items of the HDA process on the other hand is then performed to compare both solutions. This ‘‘gate-to-gate’’ environmental study is then enlarged by implementing a ‘‘cradle-togate’’ Life Cycle Assessment (LCA), for accounting of emission inventory and extraction. The use of a natural gas turbine, less economically efficient, turns out to be a more attractive alternative to meet the societal expectations concerning environment preservation and sustainable development