Publications
191 results found
Karia T, Adjiman C, Chachuat B, 2022, Assessment of a two-step approach for global optimization of mixed-integer polynomial programs using quadratic reformulation, Computers and Chemical Engineering, Vol: 165, ISSN: 0098-1354
This paper revisits the approach of transforming a mixed-integer polynomial program (MIPOP) into a mixed-integer quadratically-constrained program (MIQCP), in the light of recent progress in global solvers for this latter class of models. We automate this transformation in a new reformulation engine called CANON, alongside preprocessing strategies including local search and bounds tightening. We conduct comparative tests on a collection of 137 MIPOPs gathered from test libraries such as MINLPLib. The solver GUROBI gives the best performance on the reformulated MIQCPs and outperforms the generic global solvers BARON and SCIP. The MIQCP reformulation also improves the performance of SCIP compared to direct MIPOP solution, whereas the performance of BARON is comparable on the original MIPOPs and reformulated MIQCPs. Overall, these results establish the effectiveness of quadratic reformulation for MIPOP global optimization and support its integration into global solvers.
Kusumo K, Kuriyan K, Vaidyaraman S, et al., 2022, Probabilistic framework for optimal experimental campaigns in the presence of operational constraints, Reaction Chemistry and Engineering, Vol: 7, Pages: 2359-2374, ISSN: 2058-9883
The predictive capability of any mathematical model is intertwined with the quality of experimentaldata collected for its calibration. Model-based design of experiments helps compute maximallyinformative campaigns for model calibration. But in early stages of model development it is crucial toaccount for model uncertainties to mitigate the risk of uninformative or infeasible experiments. Thisarticle presents a new method to design optimal experimental campaigns subject to hard constraintsunder uncertainty, alongside a tractable computational framework. This computational frameworkinvolves two stages, whereby the feasible experimental space is sampled using a probabilistic approachin the first stage, and a continuous-effort optimal experiment design is determined by searching overthe sampled feasible space in the second stage. The tractability of this methodology is demonstratedon a case study involving the exothermic esterification of priopionic anhydride with significant risk ofthermal runaway during experimentation. An implementation is made freely available based on thePython packages DEUS and Pydex.
Kis Z, Tak K, Ibrahim D, et al., 2022, Pandemic-response adenoviral vector and RNA vaccine manufacturing, npj Vaccines, Vol: 7, ISSN: 2059-0105
Rapid global COVID-19 pandemic response by mass vaccination is currently limited by the rate of vaccine manufacturing. This study presents a techno-economic feasibility assessment and comparison of three vaccine production platform technologies deployed during the COVID-19 pandemic: (1) adenovirus-vectored (AVV) vaccines, (2) messenger RNA (mRNA) vaccines, and (3) the newer self-amplifying RNA (saRNA) vaccines. Besides assessing the baseline performance of the production process, impact of key design and operational uncertainties on the productivity and cost performance of these vaccine platforms is evaluated using variance-based global sensitivity analysis. Cost and resource requirement projections are computed for manufacturing multi-billion vaccine doses for covering the current global demand shortage and for providing annual booster immunisations. The model-based assessment provides key insights to policymakers and vaccine manufacturers for risk analysis, asset utilisation, directions for future technology improvements and future pidemic/pandemic preparedness, given the disease-agnostic nature of these vaccine production platforms.
Sunny N, Bernardi A, Danaci D, et al., 2022, A pathway towards net-zero emissions in oil refineries, Frontiers in Chemical Engineering, Vol: 4, ISSN: 2673-2718
Rapid industrialization and urbanization have increased the demand for both energy and mobility services across the globe, with accompanying increases in greenhouse gas emissions. This short paper analyzes strategic measures for the abatement of CO2 emissions from oil refinery operations. A case study involving a large conversion refinery shows that the use of post-combustion carbon capture and storage (CCS) may only be practical for large combined emission point sources, leaving about 30% of site-wide emissions unaddressed. A combination of post-combustion CCS with a CO2 capture rate well above 90% and other mitigation measures such as fuel substitution and emission offsets is needed to transition towards carbon-neutral refinery operations. All of these technologies must be configured to minimize environmental burden shifting and scope 2 emissions, whilst doing so cost-effectively to improve energy access and affordability. In the long run, scope 3 emissions from the combustion of refinery products and flaring must also be addressed. The use of synthetic fuels and alternative feedstocks such as liquefied plastic waste, instead of crude oil, could present a growth opportunity in a circular carbon economy.
Petsagkourakis P, Chachuat B, Rio-Chanona EAD, 2022, Safe real-time optimization using multi-fidelity guassian processes, Publisher: ArXiv
This paper proposes a new class of real-time optimization schemes to overcomesystem-model mismatch of uncertain processes. This work's novelty lies inintegrating derivative-free optimization schemes and multi-fidelity Gaussianprocesses within a Bayesian optimization framework. The proposed scheme usestwo Gaussian processes for the stochastic system, one emulates the (known)process model, and another, the true system through measurements. In this way,low fidelity samples can be obtained via a model, while high fidelity samplesare obtained through measurements of the system. This framework captures thesystem's behavior in a non-parametric fashion while driving exploration throughacquisition functions. The benefit of using a Gaussian process to represent thesystem is the ability to perform uncertainty quantification in real-time andallow for chance constraints to be satisfied with high confidence. This resultsin a practical approach that is illustrated in numerical case studies,including a semi-batch photobioreactor optimization problem.
Kusumo K, Kuriyan K, Vaidyaraman S, et al., 2022, Risk mitigation in model-based experiment design: a continuous-effort approach to optimal campaigns, Computers and Chemical Engineering, Vol: 159, ISSN: 0098-1354
A key challenge in maximizing the effectiveness of model-based design of experiments for calibrating nonlinear process models is the inaccurate prediction of information that is afforded by each new experiment. We present a novel methodology to exploit prior probability distributions of model parameter estimates in a bi-objective optimization formulation, where a conditional-value-at-risk criterion is considered alongside an average information criterion. We implement a tractable numerical approach that discretizes the experimental design space and leverages the concept of continuous-effort experimental designs in a convex optimization formulation. We demonstrate effectiveness and tractability through three case studies, including the design of dynamic experiments. In one case, the Pareto frontier comprises experimental campaigns that significantly increase the information content in the worst-case scenarios. In another case, the same campaign is proven to be optimal irrespective of the risk attitude. An open-source implementation of the methodology is made available in the Python software Pydex.
Bernardi A, Bello F, Valente A, et al., 2022, Enviro-economic assessment of DME synthesis using carbon capture and hydrogen from methane pyrolysis, Computer Aided Chemical Engineering, Pages: 1003-1008
The catalytic conversion of captured CO2 and H2 into fuels is recognised as an interesting option to decarbonise the transport sector in the short-midterm future. DME has been identified as an ideal diesel-substitute for heavy-duty vehicles due to its high cetane number and excellent combustion properties, but to be competitive with diesel a low-cost and low-carbon H2 production route is a key enabler. Recent developments indicate that methane pyrolysis has the potential to produce H2 at a similar cost compared to steam methane reforming, the main H2 production route nowadays, yet with no direct CO2 emissions. This paper presents an enviro-economic assessment of 12 life-cycle pathways for DME production. Our results show that DME produced using H2 from methane pyrolysis could be competitive with diesel, both economically and environmentally, but is highly dependent upon the utilisation of the carbon by-product.
Kis Z, Tak K, Ibrahim D, et al., 2022, Quality by design and techno-economic modelling of RNA vaccine production for pandemic-response, Computer Aided Chemical Engineering, Pages: 2167-2172
Vaccine production platform technologies have played a crucial role in rapidly developing and manufacturing vaccines during the COVID-19 pandemic. The role of disease agnostic platform technologies, such as the adenovirus-vectored (AVV), messenger RNA (mRNA), and the newer self-amplifying RNA (saRNA) vaccine platforms is expected to further increase in the future. Here we present modelling tools that can be used to aid the rapid development and mass-production of vaccines produced with these platform technologies. The impact of key design and operational uncertainties on the productivity and cost performance of these vaccine platforms is evaluated using techno-economic modelling and variance-based global sensitivity analysis. Furthermore, the use of the quality by digital design framework and techno-economic modelling for supporting the rapid development and improving the performance of these vaccine production technologies is also illustrated.
Sarkis M, Tak K, Chachuat B, et al., 2022, Towards Resilience in Next-Generation Vaccines and Therapeutics Supply Chains, Computer Aided Chemical Engineering, Pages: 931-936
Recent clinical outcomes of Advanced Therapy Medicinal Products (ATMPs) highlight promising opportunities in the prevention and cure of life threatening diseases. ATMP manufacturers are asked to tackle engineering product and process-related challenges, whilst scaling up production under demand uncertainty; this highlights the need for tools supporting supply chain planning under uncertainty. This study presents a computer-aided modelling and optimisation framework for viral vector supply chains. A methodology for the characterisation of process-related uncertainties is presented; the impact of input demand and process bottlenecks on optimal supply chain configurations and capacity allocations is assessed. A trade-off between cost and scalability emerges, larger costs incurring at higher input demands, whilst ensuring improved flexibility under demand uncertainty. Furthermore, bottlenecks uncertainty drives the optimisation to alternative strategic decisions, highlighting the need for a systematic integration within the framework.
Uribe-Rodriguez A, Castro PM, Guillén-Gosálbez G, et al., 2022, Lagrangean Decomposition for Integrated Refinery-Petrochemical Short-term Planning, Computer Aided Chemical Engineering, Pages: 583-588
We present a methodology for the optimal integration of crude management (CM) and refinery-petrochemical (RP) planning operations. The physical coupling between both CM and RP optimization subproblems is via the flow rate, physical-chemical properties, and composition of the crude blends. For a given economic cost of the crude blends, which either provides a selling price for CM or a purchase price for RP, both subproblems can maximize their profits independently. But failure to integrate these two subproblems can create an imbalance between crude supply and demand. Optimizing CM and RP operations simultaneously entails the solution of large-scale, nonconvex quadratically-constrained quadratic programs (MIQCQPs). We apply a spatial Lagrangean decomposition algorithm to tackle these MIQCQPs and demonstrate it on a full-scale industrial facility. The results show that Lagrangean decomposition can outperform commercial global solvers BARON and ANTIGONE when applied to the monolithic MIQCQP. The Lagrangean decomposition can also reduce the optimality gap faster than with a clustering decomposition algorithm, leading to optimality gaps below 5% within 1 hour of CPU time.
Ibrahim D, Kis Z, Tak K, et al., 2022, Optimal design and planning of supply chains for viral vectors and RNA vaccines, Computer Aided Chemical Engineering, Pages: 1633-1638
This work develops a multi-product MILP vaccine supply chain model that supports planning, distribution, and administration of viral vectors and RNA-based vaccines. The capability of the proposed vaccine supply chain model is illustrated using a real-world case study on vaccination against SARS-CoV-2 in the UK that concerns both viral vectors (e.g., AZD1222 developed by Oxford-AstraZeneca) and RNA-based vaccine (e.g., BNT162b2 developed by Pfizer-BioNTech). A comparison is made between the resources required and logistics costs when viral vectors and RNA vaccines are used during the SARS-CoV-2 vaccination campaign. Analysis of results shows that the logistics cost of RNA vaccines is 85% greater than that of viral vectors, and that transportation cost dominates logistics cost of RNA vaccines compared to viral vectors.
Ibrahim D, Kis Z, Tak K, et al., 2021, Model-based planning and delivery of mass vaccination campaigns against infectious disease: application to the COVID-19 pandemic in the UK, Vaccines, Vol: 9, Pages: 1-19, ISSN: 2076-393X
Vaccination plays a key role in reducing morbidity and mortality caused by infectious diseases, including the recent COVID-19 pandemic. However, a comprehensive approach that allows the planning of vaccination campaigns and the estimation of the resources required to deliver and administer COVID-19 vaccines is lacking. This work implements a new framework that supports the planning and delivery of vaccination campaigns. Firstly, the framework segments and priorities target populations, then estimates vaccination timeframe and workforce requirements, and lastly predicts logistics costs and facilitates the distribution of vaccines from manufacturing plants to vaccination centres. The outcomes from this study reveal the necessary resources required and their associated costs ahead of a vaccination campaign. Analysis of results shows that by integrating demand stratification, administration, and the supply chain, the synergy amongst these activities can be exploited to allow planning and cost-effective delivery of a vaccination campaign against COVID-19 and demonstrates how to sustain high rates of vaccination in a resource-efficient fashion.
Baaqel H, Hallett JP, Guillen-Gosalbez G, et al., 2021, Sustainability assessment of alternative synthesis routs to aprotic ionic liquids: the case of 1-Butyl-3-methylimidazolium tetrafluoroborate for fuel desulfurization, ACS Sustainable Chemistry and Engineering, Vol: 10, Pages: 323-331, ISSN: 2168-0485
Advantages of ionic liquids (ILs) over volatile organic solvents in chemical processes include no or negligible evaporative losses and high tunability. However, the conventional production of aprotic ILs via metathesis can be unattractive (both economically and environmentally) because of its high complexity, while the performance of other synthesis routes remains unclear. Existing life-cycle assessments furthermore fail to combine the production and use phases of these solvents, leading to erroneous conclusion about their sustainability credentials. This paper compares a one-pot, halide-free production route to 1-butyl-3-methylimidazolium tetrafluoroborate [BMIM][BF4] against metathesis and two conventional fuel desulfurization solvents, namely, acetonitrile and dimethylformamide (DMF). Halide-free synthesis is predicted to reduce the cost and environmental impacts associated with the production of [BMIM][BF4] by 2–5-fold compared to metathesis. Upon including the use phase of the solvents in fuel desulfurization and accounting for the uncertainty in background data, halide-free [BMIM][BF4] consistently presents the lowest cost and environmental impacts, while DMF is the worst in class. As well as exemplifying the importance of synthesis routes of ILs on their sustainability, these results highlight the need to include the use phase of solvents for more comprehensive life-cycle assessments.
Kusumo KP, Morrissey J, Mitchell H, et al., 2021, A design centering methodology for probabilistic design space, 16th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM), Publisher: Elsevier, Pages: 79-84, ISSN: 2405-8963
The use of mathematical models for design space characterization has become commonplace in pharmaceutical quality-by-design, providing a systematic risk-based approach to assurance of quality. This paper presents a methodology to complement sampling algorithms by computing the largest box inscribed within a given probabilistic design space at a desired reliability level. Such an encoding of the samples yields an operational envelope that can be conveniently communicated to process operators as independent ranges in process parameters. The first step involves training a feed-forward multi-layer perceptron as a surrogate of the sampled probability map. This surrogate is then embedded into a design centering problem, formulated as a semi-infinite program and solved using a cutting-plane algorithm. Effectiveness and computational tractability are demonstrated on the case study of a batch reactor with two critical process parameters.
Paulen R, Gomoescu L, Chachuat B, 2021, Nested sampling approach to set-membership estimation, 21st IFAC World Congress on Automatic Control - Meeting Societal Challenges, Publisher: Elsevier, Pages: 7228-7233, ISSN: 2405-8963
This paper is concerned with set-membership estimation in nonlinear dynamic systems. The problem entails characterizing the set of all possible parameter values such that given predicted outputs match their corresponding measurements within prescribed error bounds. Most existing methods to tackle this problem rely on outer-approximation techniques, which perform poorly when the parameter host set is large due to the curse of dimensionality. An adaptation of nested sampling—a Monte Carlo technique introduced to compute Bayesian evidence—is presented herein. The nested sampling algorithm leverages efficient strategies from Bayesian statistics for generating an inner-approximation of the desired parameter set. Several case studies are presented to demonstrate the approach.
Rodriguez-Vallejo DF, Valente A, Guillen-Gosalbez G, et al., 2021, Economic and life-cycle assessment of OME3-5 as transport fuel: a comparison of production pathways, Sustainable Energy & Fuels, Vol: 5, Pages: 2504-2516, ISSN: 2398-4902
Reducing the contribution of the transport sector to climate change calls for a transition towards renewable fuels. Polyoxymethylene dimethyl ethers (OMEn) constitute a promising alternative to fossil-based diesel. This article presents a comparative analysis of 17 OME3–5 production pathways, benchmarked against fossil-based diesel under environmental and economic criteria following a life-cycle approach. OME3–5 fuels that are reliant on biomass as feedstock, or use H2 produced from wind- or nuclear-powered electrolysis and CO2 from direct air capture, have the potential to reduce global warming impacts by up to 20%. Nevertheless, such fuels are also found to shift environmental burdens to other impact categories under human health and ecosystems quality due to procurement of raw materials (H2, CO2 and biomass), and their predicted total monetized cost is 1.5–3.6 times that of fossil-based diesel. These results highlight the need for embracing impacts beyond climate change in the environmental assessment of alternative fuels and including negative externalities in their economic assessment.
Jing R, Li Y, Wang M, et al., 2021, Coupling biogeochemical simulation and mathematical optimisation towards eco-industrial energy systems design, APPLIED ENERGY, Vol: 290, ISSN: 0306-2619
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- Citations: 5
Quek VC, Shah N, Chachuat B, 2021, Plant-wide assessment of high-pressure membrane contactors in natural gas sweetening – Part I: Model development, Separation and Purification Technology, Vol: 258, Pages: 1-13, ISSN: 1383-5866
This paper presents a predictive mathematical model of high-pressure membrane contactor, with a view to developing a plant-wide model of natural gas sweetening including amine regeneration. We build upon an existing model of high-pressure membrane contactor by Quek et al. [Chem Eng Res Des 132:1005–1019, 2018], which uses a combination of 1-d and 2-d mass-balance equations to predict the CO2 absorption flux and membrane wetting under lean solvent operation. For the first time, quantitative predictions of the CO2 absorption flux can be made under both lean and semi-lean operations. A 1-d energy balance that accounts for the solvent evaporative losses and the exothermic CO2 absorption into the amine is solved alongside the mass-balance equations, in order to predict the solvent temperature profile inside the contactor. The evaporative losses of water and amines can be quantified separately, as well as the absorptive losses of light hydrocarbons with the amine solvent. The model’s predictive capability is tested against data from a lab-scale module and a pilot-scale module that is operated under industrially relevant conditions at a natural gas processing facility in Malaysia. A close agreement between model predictions and measurements of the CO2 absorption flux, solvent temperature profile, and hydrocarbon loss is observed for a wide range of gas and solvent flowrates and compositions, thereby validating the modeling assumptions. The contactor model is combined in a plant-wide model of natural gas sweetening in the companion paper, where it is used for process integration and analysis.
Quek VC, Shah N, Chachuat B, 2021, Plant-wide assessment of high-pressure membrane contactors in natural gas sweetening – Part II: Process analysis, Separation and Purification Technology, Vol: 258, Pages: 1-11, ISSN: 1383-5866
This paper presents a model-based assessment of a natural gas sweetening process combining high-pressure membrane contactor with conventional amine regeneration. The analysis builds on a mathematical model of the membrane contactor developed in the companion paper, which is capable of quantitative predictions of the CO2 and hydrocarbon absorption in the amine solvent and the solvent evaporative losses to the treated gas. The predictive capability of the plant-wide model is tested against data from a pilot plant operated under industrially relevant conditions at a natural gas processing facility in Malaysia, showing a close agreement of the predictions with the CO2 outlet purity and the energy consumption at various CO2 loading in the amine solvent. This enables a model-based analysis of various operational decisions on the plant-wide solvent losses and hydrocarbon recovery from the rich amine. A new semi-lean process configuration that replaces the energy-intensive stripper column by a simple flash separator is shown to reduce the overall energy consumption significantly while still meeting sales gas specification. This new configuration forms the basis for the scale-up of a commercial natural gas sweetening process, which shows a high intensification potential in terms of volume footprint and energy duty compared to conventional amine treating plants.
Chanona EADR, Petsagkourakis P, Bradford E, et al., 2021, Real-time optimization meets Bayesian optimization and derivative-free optimization: A tale of modifier adaptation, COMPUTERS & CHEMICAL ENGINEERING, Vol: 147, ISSN: 0098-1354
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- Citations: 19
Karia T, Adjiman CS, Chachuat B, 2021, Global Optimization of Mixed-Integer Polynomial Programs via Quadratic Reformulation, Computer Aided Chemical Engineering, Pages: 655-661
Mixed-integer polynomial programs (MIPOPs) frequently arise in chemical engineering applications such as pooling, blending and operations planning. Many global optimization solvers rely on mixed-integer linear (MIP) relaxations of MIPOPs and solve them repeatedly as part of a branch-and-bound algorithm using commercial MIP solvers. GUROBI, one of the prominent MIP solvers, recently added the capability to solve mixed-integer quadratically-constrained quadratic programs (MIQCQPs). This paper investigates global optimization of MIPOPs via their reformulation as MIQCQPs followed by their solution to global optimality using GUROBI. The effectiveness of this approach is tested on 60 instances of MIPOPs selected from the library MINLPLib. The performance of the MIQCQP reformulation approach is compared to the state-of-the-art global solvers BARON, ANTIGONE and SCIP in GAMS. For the case of single threading, a reduction of 28% and 42% compared to SCIP and ANTIGONE respectively is observed. This approach, therefore, holds promise for integration into existing global solvers to handle MIPOPs.
Baaqel H, Hallett JP, Guillén-Gosálbez G, et al., 2021, Uncertainty analysis in life-cycle assessment of early-stage processes and products: a case study in dialkyl-imidazolium ionic liquids, Computer Aided Chemical Engineering, Pages: 785-790
This paper presents a methodology for combining foreground and background uncertainty in the life-cycle assessment (LCA) of processes and products at a low technology-readiness level. We compare the LCA of two ionic liquids, 1-butyl-3-methyl-imidazoliumtetrafluoroborate [bmim][BF4] and 1-butyl-3-methyl-imidazolium hexafluorophosphate [bmim][PF6]. The nominal scenario predicts that [bmim][BF4] generates lower end-point environmental impacts than [bmim][PF6]. However, the uncertainty ranges around these nominal predictions overlap significantly, with [bmim][BF4] causing higher impacts than those of [bmim][PF6] in up to 30% of the uncertainty scenarios. On top of this, accounting for uncertainty in the foreground data more than doubles the estimated impact ranges in several damage categories. This case study, therefore, demonstrates the need for combining foreground and background data uncertainty for more reliable life-cycle assessments.
Kusumo KP, Kuriyan K, García-Muñoz S, et al., 2021, Continuous-Effort Approach to Model-Based Experimental Designs, Computer Aided Chemical Engineering, Pages: 867-873
Model-based design of experiments is a technique for accelerating the development of mathematical models. Through maximally informative experiments, time and resources for estimating uncertain model parameters are minimized. This article presents a method for computing effort-based experimental designs, whereby designs are akin to experimental recipes. As well as identifying which experiments are the most informative, the optimal experimental effort to dedicate to each experiment is also optimized. Upon discretizing the experimental design space and treating the efforts as continuous decision variables, this method leads to convex optimization problems regardless of the model structure, which is ideal for large, parallel experimental campaigns. The case study of a batch reactor model with four parameters is presented to illustrate the methodology.
Medina EIS, Vallejo DR, Chachuat B, et al., 2021, Acyclic modular flowsheet optimization using multiple trust regions and Gaussian process regression, Computer Aided Chemical Engineering, Pages: 1117-1123
This paper presents an algorithm to optimize process flowsheets using Gaussian processes regression and trust regions. We exploit the modular structure of the flowsheet by training separate Gaussian processes (GPs) for each module based on data generated by a process simulator. These GPs are embedded into an optimization model, whose outcome is used to adapt the position and size of the trust region at each iteration. A complication that arises because of the multiple trust regions is that the optimization problem may become infeasible, in which case a feasibility (restoration) problem is invoked. An inherent advantage of this approach is that it removes the need for simulating the complete flowsheet at any point. We demonstrate these ideas on the case-study of an extractive distillation system in order to minimize its total annualized cost (TAC). The performance shows a robust strategy to address flowsheet optimization problems without recycles.
Shah SL, Bakshi BR, Liu J, et al., 2020, Meeting the challenge of water sustainability: The role of process systems engineering, AICHE JOURNAL, Vol: 67, ISSN: 0001-1541
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- Citations: 3
Uribe-Rodriguez A, Castro PM, Gonzalo G-G, et al., 2020, Global optimization of large-scale MIQCQPs via cluster decomposition: Application to short-term planning of an integrated refinery-petrochemical complex, Computers and Chemical Engineering, Vol: 140, Pages: 1-18, ISSN: 0098-1354
Integrated refinery-petrochemical facilities are complex systems that require advanced decision-support tools for optimal short-term planning of their operations. The problem can be formulated as a mixed-integer quadratically constrained quadratic program (MIQCQP), in which discrete decisions select operating modes for the process units, while the entire process network is represented by input-output relationships based on bilinear expressions describing yields and stream properties, pooling equations, fuels blending indices and cost indicators. We develop a novel decomposition-based algorithm for deterministic global optimization that divides the network into small clusters according to their functionality. Inside each cluster, we derive a mixed-integer linear programming (MILP) relaxation based on piecewise McCormick envelopes, dynamically partitioning the variables that belong to the cluster and reducing their domains through optimality-based bound tightening. Results for an industrial case study in Colombia show profit improvements above 10% and significantly reduced optimality gaps compared with the state-of-the-art global optimization solvers BARON and ANTIGONE.
Baqeel H, Diaz I, Tulus V, et al., 2020, Role of life-cycle externalities in the valuation of protic ionic liquids – a case study in biomass pretreatment solvents, Green Chemistry, Vol: 22, Pages: 3132-3140, ISSN: 1463-9262
Ionic liquids have found their way into many applications where they show a high potential to replace traditional chemicals. But there are concerns over their ecological impacts (toxicity and biodegradability) and high cost, which have limited their use so far. The outcome of existing techno-economic and life-cycle assessments comparing ionic liquids with existing solvents has proven hard to interpret due to the many metrics used and trade-offs between them. For the first time, this paper couples the concept of monetization with detailed process simulation and life-cycle assessment to estimate the true cost of ionic liquids. A comparative case study on four solvents used in lignocellulosic biomass pretreatment is conducted: triethylammonium hydrogen sulfate [TEA][HSO4], 1-methylimidazolium hydrogen sulfate [HMIM][HSO4], acetone from fossil sources, and glycerol from renewable sources. The results show that the total monetized cost of production accounting for externalities can be more than double the direct costs estimated using conventional economic assessment methods. The ionic liquid [TEA][HSO4] is found to have the lowest total cost, while the renewable solvent glycerol presents the highest total cost. We expect this methodology to provide a starting point for future research and development in sustainable ionic liquids
Al-Qahtani A, Gonzalez-Garay A, Bernardi A, et al., 2020, Electricity grid decarbonisation or green methanol fuel? A life-cycle modelling and analysis of today's transportation-power nexus, APPLIED ENERGY, Vol: 265, ISSN: 0306-2619
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- Citations: 15
Rodríguez-Vallejo DF, Guillén-Gosálbez G, Chachuat B, 2020, What is the true cost of producing propylene from methanol? the role of externalities, ACS Sustainable Chemistry & Engineering, Vol: 8, Pages: 3072-3081, ISSN: 2168-0485
The demand for olefins has increased steadily in recent years, with a propylene demand around 100 million tons per year and an expected annual growth of 3–4%. Most propylene is presently produced via steam cracking of naphtha, but on-purpose processes based on selective propane dehydrogenation or utilizing methanol as an intermediate are also being investigated and deployed. The coal-to-propylene route in particular has gained wide interest in China. This paper presents an assessment of such emerging propylene production routes from methanol by combining detailed process simulation with life-cycle assessment and monetization of the environmental impacts. Though presenting a competitive direct production cost, the coal-to-propylene route has by far the highest total monetized cost after accounting for the human health and ecosystem quality externalities. As for the natural-gas-to-propylene route, it has about double the total monetized cost of conventional steam cracking of naphtha or propane dehydrogenation because of high human health and resource depletion externalities. These results provide a clear indication that both the coal-to-propylene and natural-gas-to-propylene routes are unsustainable. They also highlight the importance of accounting for negative externalities in assessing the techno-economic performance of industrial processes as it can radically change the outcome of the analysis.
Mutran VM, Ribeiro CO, Nascimento CO, et al., 2020, Risk-conscious optimization model to support bioenergy investment in the Brazilian sugarcane industry, Applied Energy, Vol: 258, Pages: 1-15, ISSN: 0306-2619
The past decades have seen a diversification of the sugarcane industry with the emergence of new technology to produce bioenergy from by-product and waste process streams. Given Brazil’s ambitious goal of reducing green-house gas emissions by over 40% below 2005 levels by 2030, it is of paramount importance to develop reliable decision-making systems in order to stimulate investment in these low-carbon technologies. This paper seeks to develop a more accurate optimization model to inform risk-conscious investment decisions for bioenergy generation capacity in sugarcane mills. The main objective is for the model to enable a better understanding of how Brazilian government policies, such as the electricity price in the regulated market, may impact these investments, by taking into account the uncertainty in sugar, ethanol and spot electricity markets and the interdependency between production and investment decisions in terms of saleable product mix. The proposed methodology combines portfolio optimization theory with superstructure process modeling and it relies on simple surrogates derived from a detailed sugarcane plant simulator to retain computational tractability and enable scenario analysis. The case study of an existing sugarcane plant is used to demonstrate the methodology and illustrate how the model can assist decision-makers. In all of the scenarios assessed, the model recommends investment in extra bioelectricity capacity via the anaerobic digestion of vinasse but advises against investment in second-generation ethanol production via the hydrolysis of surplus bagasse. Furthermore, the decision to upgrade the cogeneration system with a condensation turbine is highly sensitive to the electricity price practiced in the regulated market, capacity constraints on the sugar-ethanol mix, and the accepted level of risk. Another key insight drawn from the case study is that recent market conditions have favored a production focused on the sugar business, maki
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