Search

This study introduces an innovative methodology for early-stage biorefinery design and analysis through the integration of metabolic models into superstructure optimization. The focus is on two types of metabolic models: i) those where ideal growth conditions are assumed, and ii) those where growth is dependent on environmental variables. Metabolic models offer a comprehensive and dynamic representation of the bioreactor system, unbound by the limitations of traditional kinetic models or the need of extensive experimental data. The integration of these models involves a curation and validation process, ensuring that the metabolic capabilities of the microorganisms are accurately represented. Once validated, the models are plugged into the superstructure in the form of surrogate equations that can be handled by the optimization problem for the superstructure. The effectiveness of this methodology is then tested and critically analyzed with the help of two case studies. The primary contribution of this work lies in its effective identification of the most favorable combinations of substrates and microorganisms. The outcome establishes the feasibility of utilizing different substrates and microorganisms in a biorefinery context, thereby highlighting the value of metabolic models in a superstructure optimization framework for exploring early-stage biorefinery schemes. This approach holds significant promise in enhancing the efficiency and sustainability of future biorefinery systems.

It is crucial to leave behind the traditional linear economy approach. Shifting the paradigm and adopting a circular (bio)economy seems to be the strategy to decouple economic growth from continuous resource extraction. To this end, producing bio-based products that aim to replace a part, if not all, of the fossil-based chemicals and fuels is a promising step. This can be achieved by using multi-product integrated biorefineries that convert organic wastes into chemicals, fuels, and bioenergy to optimize the use and close the materials and energy loops. To further address the development and implementation of organic waste integrated biorefineries, we proposed the open-source organic waste to value-added products (O2V) model and multi-objective optimization tool. O2V aims to provide a quick and straightforward holistic assessment, leading to identifying optimal or near-optimal design, planning, and operational decisions. This model not only prioritizes economic benefits but also takes on board the other pillars of sustainability. The proposed tool is built on a comprehensive superstructure of processing alternatives that include all stages concerning the conversion of organic waste to value-added products. Furthermore, it has been framed and formulated in a "plug-and-play" format, where, when required, the user only needs to add new process data to the structured information database. This database integrates data on (i) new processes (e.g., different conversion technologies), (ii) feedstocks (e.g., composition), and (iii) products (e.g., prices), among others. Due to Denmark's high availability of organic waste, implementing a second-generation integrated biorefinery in Denmark has been chosen as a realistic showcase. The application of O2V efficiently led to the identification of trade-offs between the different sustainability angles. Thus, it made it possible to determine early-stage decisions regarding product portfolio, optimal production process, and related pl

This manuscript describes the conceptual process design of an integrated xylitol biorefinery with value-added co-products. Based on an existing three-step framework, the main product of a second-generation integrated biorefinery is chosen in the first stage. Based upon this, other decisions as the feedstock and value-added co-products are made. All relevant unit operations for the process are introduced. An initial superstructure with all potential process alternatives is composed of all introduced models. In the second step of the framework, a global sensitivity analysis is performed, firstly with coarse sampling to determine all viable flowsheet options and secondly with fine sampling to determine the most sensitive operational variables. As a result of the sensitivity analysis, most of the flowsheet options in the initial superstructure are not feasible. Based on these results, flowsheet sampling with the five most sensitive operational variables is performed to create surrogate models. In the scope of this work, three types of surrogate models are benchmarked against each other. Regarding the results of the superstructure optimization, firstly, it becomes apparent that the production of biokerosene does not contribute significantly to the net present value of the biorefinery. Furthermore, reducing the number of unit operations in the downstream processing leads to lower capital expenditures, but it lowers the product yield. Lastly, most flowsheets are economically feasible, indicated by a positive net present value. Based on this result, the most promising candidate process topology is subjected to the third step of the framework, including uncertainty in capital expenditure and operational expenses according to their estimations and uncertainties in the product prices. As a result, the net present value of the flowsheet turns negative, indicating that the high uncertainties for the expenditure and the expenses do not allow for an economically feasible operation

Control and operation of intensified processes are inherently difficult due to a reduced region of controllability, which originates from a high degree of interaction among the design-control parameters. As a result, a design with feasible steady state operation may be dynamically inoperable due to the inability to ensure stability in the open-loop or closed-loop. To address these issues, an integrated design-control toolbox, which is tailor-made for reactive distillation (RD) processes, is proposed in this paper. Given a set of design-control parameters, this RD-Toolbox links in-house tools to required external tools and guides the user to set up steady state and dynamic simulations and perform controllability analysis. To demonstrate the range of functionalities of the RD-Toolbox, two case studies (ethyl tert-butyl ether (ETBE) and ethyl acetate production) are presented. For the ETBE case study, the design-control parameters are obtained through two integrated design-control methods, namely the superstructure optimization method and the driving force-based method. In terms of controllability, both designs for the ETBE system could efficiently reject feed disturbances. For the ethyl acetate case, the suggested set of design-control parameters corresponding to the maximum driving force-based design could exhibit satisfactory control performance. The results illustrate the advantages of using the RD-Toolbox for fast, reliable, and efficient solution and analysis of RD processes.

Advances in simulation and optimization technologies coupled with the continued growth in computing power now increasingly pave the way for the development of advanced model-based engineering design frameworks. In this work, we propose an extensive computational framework, which brings together state-of-the-art engineering practices, such as high fidelity process simulation, superstructure-based conceptual design, global sensitivity analysis, Monte Carlo procedures for uncertainty quantification, and a stochastic simulation-based design space optimizer in order to foster decision making under uncertainty. The capabilities of the framework are highlighted in a case study, which addresses the challenges of how to synthesize and design wastewater treatment plant configurations under influent uncertainties. In order to handle multiple stochastic constraints, a black-box solver using a new infill criterion for surrogate-based optimization is also proposed. The results demonstrate the promising potential of the simulation and sampling-based framework for effectively addressing stochastic design problems arising in broader engineering domains.

A key approach in expediting the transition towards a bio-based economy is the conceptual design and implementation of integrated second-generation biorefineries (iSGB). A conceptual design approach for these iSGBs is Superstructure Optimization (SSO), which yields an optimal candidate process topology (CPT) [1]. By following a simulation-based optimization (SBO) approach, complex models including inherent uncertainties can be employed in the search for an optimal process design [2]. We therefore propose a novel synergistic framework for the synthesis and design of iSGBs: based on a hybrid approach integrating surrogate-based superstructure optimization (SSO) with simulation-based design optimization (SBO). As a proof of concept, we apply this framework to a case study to the process synthesis and design of a xylitol biorefinery. Xylitol is a platform chemical and is used as sugar substitute in food industries. It can be produced by microbial fermentation from lignocellulosic biomass, which makes it an ideal product for an iSGB [3]. Consequently, succinic acid, another high-potential platform chemical- is selected as value-added co-product [4]. In order to meet the high energy demand of the iSGB, the lignin fraction is chosen as substrate for a combustion process. Mechanistic models for all necessary unit operations are developed and assessed by uncertainty and sensitivity analysis. Surrogate models are utilized for the composition of the superstructure. Performing SSO yields several CPTs, which are then subjected to SBO including the uncertainties in the model. The result of the application of the presented framework is then a consolidated base-case process for a xylitol biorefinery, which can be easily extended in the superstructure towards the evaluation of further products, process integration, plant-wide optimization or value chain optimization. The resulting process itself is evaluated against both criteria of being economically viable and sustainable

Capital investment, next to the product demand, sales, and production costs, is one of the key metrics commonly used for project evaluation and feasibility assessment. Estimating the investment costs of a new product/process alternative during early-stage design is a challenging task, which is especially relevant in biorefinery research where information about new technologies and experience with new technologies is limited. A systematic methodology for uncertainty analysis of cost data is proposed that employs: (a) bootstrapping as a regression method when cost data are available; and, (b) the Monte Carlo technique as an error propagation method based on expert input when cost data are not available. Four well-known models for early-stage cost estimation are reviewed and analyzed using the methodology. The significance of uncertainties of cost data for early-stage process design is highlighted using the synthesis and design of a biorefinery as a case study. The impact of uncertainties in cost estimation on the identification of optimal processing paths is indeed found to be profound. To tackle this challenge, a comprehensive techno-economic risk analysis framework is presented to enable robust decision-making under uncertainties. One of the results using order-of-magnitude estimates shows that the production of diethyl ether and 1,3-butadiene are the most promising with the lowest economic risks (among the alternatives considered) of 0.24 MM$/a and 4.6 MM$/a, respectively.

This paper introduces a novel MILP approach for the design of distillation columns sequences of zeotropic mixtures that explicitly include from conventional to fully thermally coupled sequences and divided wall columns with a single wall. The model is based on the use of two superstructure levels. In the upper level a superstructure that includes all the basic sequences of separation tasks is postulated. The lower level is an extended tree that explicitly includes different thermal states and compositions of the feed to a given separation task. In that way, it is possible to a priori optimize all the possible separation tasks involved in the superstructure. A set of logical relationships relates the feasible sequences with the optimized tasks in the extended tree resulting in a MILP to select the optimal sequence. The performance of the model in terms of robustness and computational time is illustrated with several examples.

This paper introduces a novel MILP approach for the design of distillation columns sequences of zeotropic mixtures that explicitly include from conventional to fully thermally coupled sequences and divided wall columns with a single wall. The model is based on the use of two superstructure levels. In the upper level a superstructure that includes all the basic sequences of separation tasks is postulated. The lower level is an extended tree that explicitly includes different thermal states and compositions of the feed to a given separation task. In that way, it is possible to a priori optimize all the possible separation tasks involved in the superstructure. A set of logical relationships relates the feasible sequences with the optimized tasks in the extended tree resulting in a MILP to select the optimal sequence. The performance of the model in terms of robustness and computational time is illustrated with several examples.