169 results found
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.
Shah SL, Bakshi BR, Liu J, et al., 2020, Meeting the challenge of water sustainability: The role of process systems engineering, AICHE JOURNAL, ISSN: 0001-1541
Rio-Chanona EAD, Petsagkourakis P, Bradford E, et al., 2020, Modifier adaptation meets bayesian optimization and derivative-free optimization, Publisher: arXiv
This paper investigates a new class of modifier-adaptation schemes toovercome plant-model mismatch in real-time optimization of uncertain processes.The main contribution lies in the integration of concepts from the areas ofBayesian optimization and derivative-free optimization. The proposed schemesembed a physical model and rely on trust-region ideas to minimize risk duringthe exploration, while employing Gaussian process regression to capture theplant-model mismatch in a non-parametric way and drive the exploration by meansof acquisition functions. The benefits of using an acquisition function,knowing the process noise level, or specifying a nominal process model areillustrated on numerical case studies, including a semi-batch photobioreactoroptimization problem.
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
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
Kusumo KP, Gomoescu L, Paulen R, et al., 2020, Nested Sampling Strategy for Bayesian Design Space Characterization, Computer Aided Chemical Engineering, Pages: 1957-1962
© 2020 Elsevier B.V. Design space is a key concept in pharmaceutical quality by design, providing better understanding of manufacturing processes and enhancing regulatory flexibility. It is of paramount importance to develop computational techniques for providing quantitative representations of a design space, in accordance with the ICH Q8 guideline. The focus is on Bayesian approaches to design space characterization, which rely on a process model to determine a feasibility probability that is used for measuring reliability and risk. The paper presents three improvements over an existing nested sampling method: two-phase strategy with the first phase using a cheap sorting function based on nominal model parameters; dynamic sampling strategy to refine the target design space; and vectorization to evaluate costly functions in parallel. These improvements are implemented as part of the python package DEUS and demonstrated on an industrial case study.
Baaqel H, Tulus V, Chachuat B, et al., 2020, Uncovering the True Cost of Ionic Liquids using Monetization, Computer Aided Chemical Engineering, Pages: 1825-1830
© 2020 Elsevier B.V. Due to their attractive properties, ionic liquids have found their way into many applications where they show high potential to replace existing chemicals. However, rising concerns over their ecological impacts, e.g., toxicity and biodegradability, and high cost have limited their use. Techno-economic and life cycle assessment studies were carried out to compare ionic liquids with existing solvents, yet the outcome of these analyses is often hard to interpret, as multiple metrics need to be considered simultaneously between which trade-offs exist. Here, for the first time the concept of monetization is coupled with process simulation and life cycle assessment to estimate the true cost of four lignocellulosic biomass pretreament solvents: triethylammonium hydrogen sulfate [TEA][HSO4], 1-methylimidazolium hydrogen sulfate [HMIM][HSO4], acetone from fossil sources and glycerol from renewable sources. The results show that monetized cost can be higher than or as high as the production cost. The real cost of production accounting for externalities can be more than 100% of direct costs estimated using conventional economic assessment methods. Our results show that [TEA][HSO4] has the lowest cost, while glycerol has the highest cost. We expect this to be a starting point for future studies targeting the design of more sustainable ionic liquids.
Uribe-Rodriguez A, Castro PM, Chachuat B, et al., 2020, Global Optimization of Refinery – petrochemical Operations via Process Clustering Decomposition, Computer Aided Chemical Engineering, Pages: 1297-1302
© 2020 Elsevier B.V. We consider the short-term planning of an integrated refinery and petrochemical complex using a mixed-integer nonlinear optimization. The process network is represented by input-output relationships based on bilinear and trilinear expressions to estimate yields and stream properties, fuels blending indices and cost functions. Binary variables select the operating modes for the process units. Our global optimization algorithm decomposes the network into small clusters according to their functionality. For the constraints inside a given cluster, we formulate a mixed-integer linear relaxation based on piecewise McCormick envelopes. The partitions for the variables are updated dynamically and their domain is reduced applying optimality-based bound tightening. For the constraints outside the cluster, we use the standard McCormick envelopes. Our approach is demonstrated on an industrial-size case study representing a typical planning scenario in Colombia. Results show that it outperforms the state-of-the-art commercial solvers ANTIGONE and BARON not only in terms of optimality gap (8 vs. 58 and 48%, respectively) but also the quality of the solution itself.
Bernardi A, Chen Y, Chadwick D, et al., 2020, Direct DME Synthesis from Syngas: a Technoeconomic Model-based Investigation, Computer Aided Chemical Engineering, Pages: 655-660
© 2020 Elsevier B.V. Dymethyl ether (DME) is of industrial interest since it is used as a precursor in many other chemical processes and it can be used as fuel in diesel engines. Nowadays, the main route to produce DME is a two-step process in which a methanol dehydration unit is connected to a methanol synthesis plant (indirect synthesis). Combining methanol synthesis and dehydration in a single reactor (direct synthesis) has attracted significant attention in recent years as it offers a theoretically higher syngas conversion per pass but leads to a more challenging downstream separation. The main contribution of this paper is a model-based comparison between an indirect DME process and two direct DME processes: a standard reactor/separation/recycle process and a once-through configuration where the unreacted syngas is used to co-produce electricity. The key-performance indicators in our analysis are the break-even price of DME, the carbon efficiency, and the energy return on energy invested. The results suggest that indirect and direct DME synthesis have similar performances both in economic terms, and in carbon and energy efficiencies terms.
Kusumo KP, Gomoescu L, Paulen R, et al., 2019, Bayesian approach to probabilistic design space characterization: a nested sampling strategy, Industrial & Engineering Chemistry Research, Vol: 59, Pages: 2396-2408, ISSN: 0888-5885
Quality by design in pharmaceutical manufacturing hinges on computational methods and tools that are capable of accurate quantitative prediction of the design space. This paper investigates Bayesian approaches to design space characterization, which determine a feasibility probability that can be used as a measure of reliability and risk by the practitioner. An adaptation of nested sampling—a Monte Carlo technique introduced to compute Bayesian evidence—is presented. The nested sampling algorithm maintains a given set of live points through regions with increasing probability feasibility until reaching a desired reliability level. It furthermore leverages efficient strategies from Bayesian statistics for generating replacement proposals during the search. Features and advantages of this algorithm are demonstrated by means of a simple numerical example and two industrial case studies. It is shown that nested sampling can outperform conventional Monte Carlo sampling and be competitive with flexibility-based optimization techniques in low-dimensional design space problems. Practical aspects of exploiting the sampled design space to reconstruct a feasibility probability map using machine learning techniques are also discussed and illustrated. Finally, the effectiveness of nested sampling is demonstrated on a higher-dimensional problem, in the presence of a complex dynamic model and significant model uncertainty.
González-Garay A, Pozo C, Galán-Martín Á, et al., 2019, Assessing the performance of UK universities in the field of chemical engineering using data envelopment analysis, Education for Chemical Engineers, Vol: 29, Pages: 29-41, ISSN: 1749-7728
University rankings have become an important tool to compare academic institutions within and across countries. Yet, they rely on aggregated scores based on subjective weights which render them sensitive to experts’ preferences and not fully transparent to final users. To overcome this limitation, we apply Data Envelopment Analysis (DEA) to evaluate UK universities in the field of chemical engineering as a case study, using data retrieved from two national rankings. DEA is a non-parametric approach developed for the multi-criteria assessment of entities that avoids the use of subjective weightings and aggregated scores; this is accomplished by calculating an efficiency index, on the basis of which universities can be classified as either ‘efficient’ or ‘inefficient’. Our analysis shows that the Higher Education Institutions (HEI) occupying the highest positions in the chemical engineering rankings might not be the most efficient ones, and vice versa, which highlights the need to complement the use of rankings with other analytical tools. Overall, DEA provides further insight into the assessment of HEIs, allowing institutions to better understand their weaknesses and strengths, while pinpointing sources of inefficiencies where improvement efforts must be directed.
Wang Y, Markides CN, Chachuat B, 2019, Optimization-based investigations of a two-phase thermofluidic oscillator for low-grade heat conversion, BMC Chemical Engineering, Vol: 1, ISSN: 2524-4175
BackgroundThe non-inertive-feedback thermofluidic engine (NIFTE) is a two-phase thermofluidic oscillator capable of utilizing heat supplied at a steady temperature to induce persistent thermal-fluid oscillations. The NIFTE is appealing in its simplicity and ability to operate across small temperature differences, reported as low as 30 ∘C in early prototypes. But it is also expected that the NIFTE will exhibit low efficiencies relative to conventional heat recovery technologies that target higher-grade heat conversion. Mathematical modeling can help assess the full potential of the NIFTE technology.ResultsOur analysis is based on a nonlinear model of the NIFTE, which we extend to encompass irreversible thermal losses. Both models predict that a NIFTE may exhibit multiple cyclic steady states (CSS) for certain design configurations, either stable or unstable, a behavior that had never been hypothesized. A parametric analysis of the main design parameters of the NIFTE is then performed for both models. The results confirm that failure to include the irreversible thermal losses in the NIFTE model can grossly overpredict its performance, especially over extended parameter domains. Lastly, we use the NIFTE model with irreversible thermal losses to assess the optimization potential of this technology by conducting a multi-objective optimization. Our results reveal that most of the optimization potential is achievable via targeted modifications of three design parameters only. The Pareto frontier between exergetic efficiency and power output is also found to be highly sensitive to these optimized parameters.ConclusionsThe NIFTE is of practical relevance to a range of applications, including the development of solar-driven pumps to support small-holder irrigation in the developing world. Given its low capital cost, potential improvements greater than 50% in efficiency or power output are significant for the uptake of this technology. Conventional heat recovery technologies a
Ogunnaike BA, Chachuat B, 2019, Preface to the Dominique Bonvin Festschrift, INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, Vol: 58, Pages: 13421-13422, ISSN: 0888-5885
Bernardi A, Gomoescu L, Wang J, et al., 2019, Kinetic Model Discrimination for Methanol and DME Synthesis using Bayesian Estimation, 12th International-Federation-of-Automatic-Control (IFAC) Symposium on Dynamics and Control of Process Systems including Biosystems (DYCOPS), Publisher: ELSEVIER SCIENCE BV, Pages: 335-340, ISSN: 2405-8963
Villanueva ME, Feng X, Paulen R, et al., 2019, Convex Enclosures for Constrained Reachability Tubes, 12th International-Federation-of-Automatic-Control (IFAC) Symposium on Dynamics and Control of Process Systems including Biosystems (DYCOPS), Publisher: ELSEVIER, Pages: 118-123, ISSN: 2405-8963
Chachuat B, Bernard O, Normey-Rico JE, 2019, 12th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems DYCOPS 2019 Florianopolis, Brazil, 23-26 April 2019 FOREWORD, 12th International-Federation-of-Automatic-Control (IFAC) Symposium on Dynamics and Control of Process Systems including Biosystems (DYCOPS), Publisher: ELSEVIER, Pages: VI-VI, ISSN: 2405-8963
Chanona EADR, Alves Graciano JE, Bradford E, et al., 2019, Modifier-Adaptation Schemes Employing Gaussian Processes and Trust Regions for Real-Time Optimization, 12th International-Federation-of-Automatic-Control (IFAC) Symposium on Dynamics and Control of Process Systems including Biosystems (DYCOPS), Publisher: ELSEVIER, Pages: 52-57, ISSN: 2405-8963
Bernardi A, Graciano JEA, Chachuat B, 2019, Production of chemicals from syngas: an enviro-economic model-based investigation, Editors: Kiss, Zondervan, Lakerveld, Ozkan, Publisher: ELSEVIER SCIENCE BV, Pages: 367-372, ISBN: 978-0-12-819939-8
Rodriguez-Vallejo DF, Galan-Martin A, Guillen-Gosalbez G, et al., 2019, Targeting of sustainable chemical processes using data envelopment analysis: application to liquid fuels for transportation, Editors: Kiss, Zondervan, Lakerveld, Ozkan, Publisher: ELSEVIER SCIENCE BV, Pages: 331-336, ISBN: 978-0-12-819939-8
Mutran VM, Ribeiro CO, Nascimento COA, et al., 2019, Risk-conscious approach to optimizing bioenergy investments in the Brazilian sugarcane industry, Editors: Kiss, Zondervan, Lakerveld, Ozkan, Publisher: ELSEVIER SCIENCE BV, Pages: 361-366, ISBN: 978-0-12-819939-8
Baaqel H, Guillén-Gosálbez G, Diaz I, et al., 2019, Estimating “true” cost of ionic liquids (ILs) using monetization
Sun M, Villanueva M, Pistikopoulos EN, et al., 2019, Methodology for robust multi-parametric control in linear continuous-time systems, Journal of Process Control, Vol: 73, Pages: 58-74, ISSN: 0959-1524
This paper presents an extension of the recent multi-parametric (mp-)NCO-tracking methodology by Sun et al. [Comput. Chem.Eng. 92:64-77, 2016] for the design of robust multi-parametric controllers for constrained continuous-time linear systems in thepresence of uncertainty. We propose a robust-counterpart formulation and solution of multi-parametric dynamic optimization (mp-DO), whereby the constraints are backed-offbased on a worst-case propagation of the uncertainty using either interval analysis orellipsoidal calculus and an ancillary linear state feedback. We address the case of additive uncertainty, and we discuss approachesto dealing with multiplicative uncertainty that retain tractability of the mp-NCO-tracking design problem, subject to extra conser-vativeness. In order to assist with the implementation of these controllers, we also investigate the use of data classifiers based ondeep learning for approximating the critical regions in continuous-time mp-DO problems, and subsequently searching for a criticalregion during on-line execution. We illustrate these developments with the case studies of a fluid catalytic cracking (FCC) unit anda chemical reactor cascade.
Houska B, Chachuat B, 2019, Global optimization in Hilbert space, Mathematical Programming, Vol: 173, Pages: 221-249, ISSN: 0025-5610
We propose a complete-search algorithm for solving a class of non-convex, possibly infinite-dimensional, optimization problems to global optimality. We assume that the optimization variables are in a bounded subset of a Hilbert space, and we determine worst-case run-time bounds for the algorithm under certain regularity conditions of the cost functional and the constraint set. Because these run-time bounds are independent of the number of optimization variables and, in particular, are valid for optimization problems with infinitely many optimization variables, we prove that the algorithm converges to an (Formula presented.)-suboptimal global solution within finite run-time for any given termination tolerance (Formula presented.). Finally, we illustrate these results for a problem of calculus of variations.
Rodríguez-Vallejo DF, Galán-Martín Á, Guillén-Gosálbez G, et al., 2018, Data envelopment analysis approach to targeting in sustainable chemical process design: Application to liquid fuels, AIChE Journal, ISSN: 0001-1541
© 2018 American Institute of Chemical Engineers This article presents a framework for combining data envelopment analysis with process systems engineering tools, aiming to improve the sustainability of chemical processes. Given a set of chemical processes, each characterized by performance indicators, the framework discriminates between efficient and inefficient processes in regard to these indicators. We develop an approach to quantifying the closest targets for an inefficient process to become efficient, while preventing unrealistic targets by accounting for thermodynamic limitations represented as mass and energy flow constraints. We demonstrate the capabilities of the framework by assessing a methanol production process with captured CO2 and fossil-based H2, in comparison to 10 alternatives. The methanol fuel is found to be suboptimal in comparison with other fuels. Making it competitive would require a significant (unrealistic in the short term) reduction in H2 price. Alternatively, the methanol fuel could become competitive upon combining fossil-based H2 with sustainably produced H2 via wind-powered electrolysis. © 2018 American Institute of Chemical Engineers AIChE J, 00: 000–000, 2018.
Graciano JEA, Chachuat B, Alves RMB, 2018, Enviro-economic assessment of thermochemical polygeneration from microalgal biomass, Journal of Cleaner Production, Vol: 203, Pages: 1132-1142, ISSN: 0959-6526
This paper presents a model-based assessment of the thermochemical conversion of microalgal biomass into Fischer-Tropsch liquids, hydrogen and electricity through polygeneration. Two novel conceptual plants are investigated, which are both comprised of the same operation units (gasification, water-gas shift, Fischer-Tropsch synthesis, upgrading, separation, Rankine cycle and gas turbines) and mainly differ in the location of the water-gas-shift unit. Both plants are found to present a carbon efficiency greater than conventional biomass-to-liquid processes. The most profitable plants in terms of the saleable products yields ca. 0.23 m3 (1.4 bbl) of liquid transportation fuels (gasoline, kerosene and diesel), ca. 16 kg of hydrogen (716.8 scm), and ca. 1.55 × 109 J (430 kW·h) of electricity per 1000 kg of dried microalgae. The corresponding displaced fossil fuels could offset the carbon emissions by 0.56 kg of carbon dioxide for every kg of processed dried microalgae. Nevertheless, predicted break-even prices are lower than 40 USD per ton of dried microalgae in the base case scenario, which is at least 10 times cheaper than the current best scenario for microalgal biomass production. These low prices are a major impediment to the viability of these thermochemical polygeneration plants, albeit presenting a good potential toward cleaner liquid fuel production.
Villanueva ME, Chachuat B, Houska B, 2018, Robust optimal feedback control for periodic biochemical processes, 10th IFAC Symposium on Advanced Control of Chemical Processes ADCHEM 2018, Publisher: IFAC Secretariat, Pages: 756-761, ISSN: 2405-8963
This paper is concerned with optimal feedback control synthesis for periodic processes with economic control objectives. The focus is on tube-based methods which optimize over robust forward invariant tubes (RFITs) in order to determine the nonlinear feedback law. The main contribution is an approach to conservatively approximating this set-based periodic feedback control optimization problem by a tractable optimal control problem, which can be solved with existing optimal control solvers. The approach is applied to an uncertain periodic biochemical production process, where the objective is to maximize the profit subject to robust safety constraints.
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