Benoit Chachuat

Imperial College London | Imperial · Department of Chemical Engineering

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) v...

Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning. This opens up an opportunity to use modern machine learning tools on large-scale nonlinear monitoring and control problems. This article provides a survey of recent resul...

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...

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 CO 2 emissions from oil refinery operations. A case study involving a large conversion refinery shows th...

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 formu...

The predictive capability of any mathematical model is intertwined with the quality of experimental data collected for its calibration. Model-based design of experiments helps compute maximally informative campaigns for model...

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...

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 increas...

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-co...

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....

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 suppor...

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 fr...

This paper proposes a new class of real-time optimization schemes to overcome system-model mismatch of uncertain processes. This work's novelty lies in integrating derivative-free optimization schemes and multi-fidelity Gaussian processes within a Bayesian optimization framework. The proposed scheme uses two Gaussian processes for the stochastic sy...

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) v...

Process industry remains one of the difficult-to-decarbonise sectors globally. To mitigate industrial greenhouse gas (GHGs) emissions, an eco-industrial energy systems (e-IES) optimisation framework is proposed by coupling mathematical optimisation with clustering algorithms and first principle modelling. Within the framework, a rooftop farming dat...

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 environmen...

A brief video introducing principles of optimal experimental design, and a solution approach for computing continuous effort experimental designs. Presented as a pre-recorded+ session of Advances in Computational Methods and Numerical Analysis at the Virtual 2020 AIChE Annual Meeting. URL: https://aiche.confex.com/aiche/2020/meetingapp.cgi/Paper/6...

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...

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 i...

This paper investigates a new class of modifier-adaptation schemes to overcome plant-model mismatch in real-time optimization of uncertain processes. The main contribution lies in the integration of concepts from the fields of Bayesian optimization and derivative-free optimization. The proposed schemes embed a physical model and rely on trust-regio...

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 use...

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 t...

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 reci...

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]. T...

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 des...

This white paper is the result of discussions during the FIPSE‐4 conference (http://fi-in-pse.org) in June 2018. It aims to highlight open problems and provide directions for future research in the area of water with emphasis on its agricultural usages. Some of the open problems discussed are: (a) the use of ecosystems as unit operations to underst...

This paper investigates a new class of modifier-adaptation schemes to overcome plant-model mismatch in real-time optimization of uncertain processes. The main contribution lies in the integration of concepts from the areas of Bayesian optimization and derivative-free optimization. The proposed schemes embed a physical model and rely on trust-region...

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 pra...

Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning. This opens up an opportunity to use modern machine learning tools on large-scale nonlinear monitoring and control problems. This article provides a survey of recent resul...

Methanol from captured CO2 provides a more sustainable alternative to gasoline due to its low carbon footprint, yet it requires a large amount of renewable energy that could be used instead to decarbonise the electricity mix. A techno-economic and environmental analysis of methanol produced from captured CO2 and renewable energy is conducted to she...

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...

Ionic liquids have found their way into many applications where they show a high potential to replace traditional chemicals. But concerns over their ecological impacts (toxicity and biodegradability) and high cost have limited their use so far. The outcome of existing techno-economic and life-cycle assessments comparing ionic liquids with existing...

Olefins demand has increased steadily in recent years, with a propylene demand around 100 and an expected annual growth between 3-4%. A majority of this propylene production is presently via steam cracking of naphtha but on-purpose processesbased on selective propane dehydrogenation or utilizing methanol as an intermediate are being deployed. The c...

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 syste...

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 Ba...

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...

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...

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 selec...

Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning. This opens up an opportunity to use modern machine learning tools on large-scale nonlinear monitoring and control problems. This article provides a survey of recent resul...

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,...

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 pra...

Background The 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...

This paper investigates modifier-adaptation schemes based on Gaussian processes to handle plant-model mismatch in real-time optimization of uncertain processes. Building upon the recent work by Ferreira et al. [European Control Conference, 2018], we present two improved algorithms that rely on trust-region ideas in order to speed-up and robustify t...

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 evaluat...

This paper is concerned with computing enclosures for the constrained reachable set of uncertain nonlinear dynamic systems. Our main contribution is a nontrivial extension of the generalized differential inequality, proposed in Villanueva et al. (2015), for the case that an a priori enclosure, of the reachable set is available. A practical implemen...

This paper presents an extension of the recent multi-parametric (mp-)NCO-tracking methodology by Sun et al. [Comput. Chem. Eng. 92 (2016) 64–77] for the design of robust multi-parametric controllers for constrained continuous-time linear systems in the presence of uncertainty. We propose a robust-counterpart formulation and solution of multi-parame...

Three parameter estimation methods are compared for the discrimination between two kinetic models of methanol and DME synthesis over Cu/ZnO/Al2O3 catalysts. Two methods apply Bayes’ rule and Monte Carlo sampling to approximate the posterior distribution, while the third one is the popular frequentist method of finding a maximum likelihood estimate...

This paper 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 regarding those indicator...

This paper introduces set-membership nonlinear regression (SMR), a new approach to nonlinear regression under uncertainty. The problem is to determine the subregion in parameter space enclosing all (global) solutions to a nonlinear regression problem in the presence of bounded uncertainty on the observed variables. Our focus is on nonlinear algebra...

Many biological systems exhibit oscillations in relation to key physiological or cellular functions, such as circadian rhythms, mitosis and DNA synthesis. Mathematical modelling provides a powerful approach to analysing these biosystems. Applying parameter estimation methods to calibrate these models can prove a very challenging task in practice, d...

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, se...

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 p...

This paper presents a model-based analysis of a process coupling tri-reforming and Fischer-Tropsch technologies for the production of liquid fuels from CO2-rich natural gas. The process also includes an upgrading section based on hydrocracking, a separation section, a water gas shift unit, and a Rankine cycle unit for recovering the excess thermal...

Over the past decade, membrane contactors (MBC) for CO2 absorption have been widely recognized for their large intensification potential compared to conventional absorption towers. MBC technology uses microporous hollow-fiber membranes to enable effective gas and liquid mass transfer, without the two phases dispersing into each other. The main cont...

This paper is concerned with the synthesis of membranes networks for gas separation using a surrogate-based optimization approach. The developed methodology accounts for the main sources of non-ideality in membrane processes based on a mechanistic model. These non-idealities are typically neglected in membrane network synthesis formulations, which...

This work describes a simple algorithm based on partial least square (PLS) to enable the construction of surrogate models using a single tuning parameter. The proposed algorithm is illustrated with the case study of a membrane module for natural gas sweetening, where a mechanistic model is used as data generator. The effect of the tuning parameter...

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 p...