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Self‐optimization of organic syntheses in an automated‐flow microreactor system using Simplex and DoE
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A recently described C(sp³)–H activation reaction to synthesise aziridines was used as a model reaction to demonstrate the methodology of developing a process model using model-based design of experiments (MBDoE) and self-optimisation approaches in flow. The two approaches are compared in terms of experimental efficiency. The self-optimisation approach required the least number of experiments to reach the specified objectives of cost and product yield, whereas the MBDoE approach enabled a rapid generation of a process model.
An automated, droplet-flow microfluidic system explores and optimizes Pd-catalyzed Suzuki–Miyaura cross-coupling reactions. A smart optimal DoE-based algorithm is implemented to increase the turnover number and yield of the catalytic system considering both discrete variables—palladacycle and ligand—and continuous variables—temperature, time, and loading—simultaneously. The use of feedback allows for experiments to be run with catalysts and under conditions more likely to produce an optimum; consequently complex reaction optimizations are completed within 96 experiments. Response surfaces predicting reaction performance near the optima are generated and validated. From the screening results, shared attributes of successful precatalysts are identified, leading to improved understanding of the influence of ligand selection upon transmetalation and oxidative addition in the reaction mechanism. Dialkylbiarylphosphine, trialkylphosphine, and bidentate ligands are assessed.
The integration of flow systems with statistical design of experiments is emerging as a valuable strategy to develop new synthetic routes towards relevant building blocks, chemical probes, and drug compounds. Optimization by experimental design incorporates statistical algorithms, mathematical models and equations, predicting tools, feedback control, and validation to generate new optimal conditions. Continuous-flow chemistry is ideally suited for this scope, as the integration of in-line analysis is simple; experimental parameters such as temperature, pressure, and flow rate can be easily controlled and fine-regulated; and automation of reaction screening can be accomplished with software assistance. This review article aims to illustrate how the combination of flow synthesizers and design of experiments can be profitable to speed up the development and optimization of more efficient, safer, and reproducible protocols for modern synthetic methods and manufacturing processes.
This article outlines the benefits of using 'Design of Experiments' (DoE) optimisation during the development of new synthetic methodology. A particularly important factor in the development of new chemical reactions is the choice of solvent which can often drastically alter the efficiency and selectivity of a process. Whilst solvent optimisation is usually done in a non-systematic way based upon a chemist's intuition and previous laboratory experience, we illustrate how optimisation of the solvent for a reaction can be carried out by using a 'map of solvent space' in a DoE optimisation. A new solvent map has been developed specifically for optimisation of new chemical reactions using principle component analysis (PCA) incorporating 136 solvents with a wide range of properties. The new solvent map has been used to identify safer alternatives to toxic/hazardous solvents, and also in the optimisation of an SNAr reaction.
The technique of Design of Experiments (DOE) was employed to facilitate the rapid automated optimisation of amide formation using a polymer supported carbodiimide system. Using an optimised set of reaction conditions, an array of 80 compounds was synthesised in a 96-well plate with the reagent being delivered via an IRORI(TM) kan to each individual well.
This paper presents the first overview of recent developments in techniques and methods that enable closed-loop optimization, also sometimes called 'self optimization', as well as discovery in different areas of molecular sciences. The closed-loop experimental platforms offer tremendous new opportunities by significantly increasing productivity, as well as enabling completely new types of experiments to be performed. Such experiments involve three main enabling technology areas: automated experimental systems, analytical instruments connected to automated chemoinformatics software and optimization or decision-making algorithms. We review the most exciting developments concerning robotic experiments, 3D printed lab-ware, experimental systems with multiple analytical instruments and advanced optimization algorithms based on machine learning approaches. A range of different chemical problems is described, which show the breadth of potential applications of this emerging experimental approach.
This paper describes the next stage in our development of self-optimising reactors. We demonstrate that the same reaction can be optimised for a series of different criteria including yield, space-time yield, E factor and a weighted yield function (the product of space-time yield and yield). In different experiments, we achieved 97.6% yield, space-time yield of 42.9 kg/L/h and E factors of 1.4 and 3.3 (including CO2) and the weighted yield, which gave a promising balance between yield, E factor and space-time yield.
Design of Experiments (DoE) has been used to optimize a diversity oriented palladium catalyzed cascade Heck-Suzuki reaction for the construction of 3-alkenyl substituted cyclopenta[b]indole compounds. The obtained DoE model revealed a reaction highly dependent on the ligand. Guided by the model, an optimal ligand was chosen that selectively delivered the desired products in high yields. The conditions were applicable with a variety of boronic acids and were used to synthesize a library of 3-alkenyl derivatized compounds. Focusing on inhibition of kinases relevant for combating melanoma, the library was used in an initial structure-activity survey. In line with the observed kinase inhibition, cellular studies revealed one of the more promising derivatives to inhibit cell proliferation via an apoptotic mechanism.
A configurable platform for synthetic chemistry incorporating an in-line benchtop NMR that is capable of monitoring and controlling organic reactions in real time is presented. The platform is controlled via a modular LabView software control system for the hardware, NMR, data analysis and feedback optimization. Using this platform we report the real-time advanced structural characterization of reaction mixtures, including 19F, 13C, DEPT, 2D NMR spectroscopy (COSY, HSQC and 19F-COSY) for the first time. Finally, the potential of this technique is demonstrated through the optimization of a catalytic organic reaction in real-time, showing its applicability to self-optimizing systems using criteria such as stereoselectivity, multi-nuclear measurements or 2D correlations.
An efficient method for the C3-glucuronidation of bile acids has been developed under flow conditions. A modular mesoreactor assisted flow set-up was combined with statistical design of experiments to speed up the optimization of the Koenigs-Knorr reaction in terms of yield, regioselectivity, costs, as well as technical and practical standpoints. Using the optimal conditions, selective glucuronidation of naturally occurring bile acids was successfully achieved offering a new, valuable route to C3-glucuronidated bile acids useful for biological, diagnostic and PK/ADMET investigations.
Here we describe the use of a new open-source software package and a Raspberry Pi(®) computer for the simultaneous control of multiple flow chemistry devices and its application to a machine-assisted, multi-step flow preparation of pyrazine-2-carboxamide - a component of Rifater(®), used in the treatment of tuberculosis - and its reduced derivative piperazine-2-carboxamide.
Pyridine–oxazoline-type ligands (PyOX) are an important class of chiral ligands for metal-catalyzed asymmetric transformations. Herein we describe an efficient and reliable flow route which is amenable for the synthesis of PyOX ligands at a scale of hundreds of milligrams per hour. Optimal flow conditions were rapidly identified through the assistance of an in-house built autonomous self-optimizing system integrating a custom-made optimization algorithm derived from the Nelder–Mead and golden section search methods. The preparation of a small library of representative PyOX ligands highlights the practical application of this flow route which should be of primary interest for synthetic chemists developing metal-catalyzed asymmetric transformations.
This work presents and evaluates an approach to obtain and model kinetic data by combining a microreactor setup and real-time reaction monitoring through inline FT-IR spectroscopy with nonsteady-state conditions and self-modeling curve resolution (SMCR). Two model reactions – imine synthesis of benzaldehyde with benzylamine and deprotonation reaction with n-butyllithium – serve as proof of concept and additionally demonstrate the method’s broad range of application, which includes simple reactions as well as complex mechanisms. Subsequent replications of the model reactions above (in terms of collection and modeling of kinetic data) using a more common approach (steady-state conditions and spectra evaluation using calibration curves) outline that the presented approach possesses greater time-efficiency compared to traditional methods (based on batch or steady-state studies), but that reliability of the resulting kinetic parameters should be reviewed carefully. However, when quick estimates are needed (analyzing the elementary reaction mechanism rather than developing a detailed scale-up model), research and industry alike may achieve significant time and cost savings through applying the outlined approach. To guide them in using this method in the most effective manner, this paper concludes by comparing two types of SMCR – soft- and hard-modeling – and argues for combining them.
The Internet of Things IoT, Industry 4.0, and the Digitalization of business processes offer new opportunities and business models for the process industry, including education and training. The ProcessNet Symposion in Tutzing, Germany, dealt in April 2018 with digitalization in the process industry. The outcome was cast in 12 theses. Lean processes and integrated, software-assisted workflows are essential foundations, on which platform solutions and digital tools such as data analysis, networking systems, and artificial intelligence can be based. The digital twin of modular equipment, processes or entire plants improves planning processes and simplifies work flow in production. Interdisciplinary communication, the acquisition and evaluation of knowledge and lifelong learning will become more and more important.
A self-optimizing reactor
Chemists spend a great deal of time tweaking the conditions of known reactions. Small changes to temperature and concentration can have a big influence over product yield. Bédard et al. present a flow-based reaction platform that carries out this laborious task automatically. By using feedback from integrated analytics, the system converges on optimal conditions that can then be applied with high precision afterward. A series of modules with heating, cooling, mixing, and photochemical capabilities could be configured for a broad range of reactions. These include homogeneous and heterogeneous palladium-catalyzed cross-coupling, reductive amination, and the generation of sensitive intermediates under an inert atmosphere.
Science , this issue p. 1220
A modular autonomous flow reactor combining monitoring technologies with a feedback algorithm is presented for the synthesis of natural product carpanone. The autonomous self-optimizing system, controlled via MATLAB®, was designed as a flexible platform enabling an adaptation of the experimental setup to the specificity of the chemical transformation to be optimized. The reaction monitoring uses either on-line high pressure liquid chromatography (HPLC) or in-line benchtop nuclear magnetic resonance (NMR) spectroscopy. The custom-made optimization algorithm derived from the Nelder-Mead and golden section search methods performs constrained optimizations of black-box functions in a multi-dimensional search domain, thereby, assuming no a priori knowledge of the chemical reactions. This autonomous self-optimizing system allowed fast and efficient optimizations of the chemical steps leading to carpanone. This contribution is the first example of a multi-step synthesis optimized with an autonomous flow reactor.
Flow chemistry involves the use of channels or tubing to conduct a reaction in a continuous stream rather than in a flask. Flow equipment provides chemists with unique control over reaction parameters enhancing reactivity or in some cases enabling new reactions. This relatively young technology has received a remarkable amount of attention in the past decade with many reports on what can be done in flow. Until recently, however, the question, “Should we do this in flow?” has merely been an afterthought. This review introduces readers to the basic principles and fundamentals of flow chemistry and critically discusses recent flow chemistry accounts.
Improved safety is one of the main drivers for microreactor application in chemical process development and small-scale production. Typical examples of hazardous chemistry are presented indicating potential risks also in miniaturized equipment. Energy balance and kinetic parameters describe the heat production potential and together with heat transfer capability the temperature development in the continuous flow reactor. Beside these calculation procedures checklists for laboratory safety and risk assessment are the basis for improved laboratory work as well as for equipment-related safety discussions. For complete and larger chemical plants, hazard and operation studies (HAZOP) are the appropriate method of handling hazardous processes and their scale-up.
The study deals with technological aspects of the reductive amination of benzaldehyde with ammonia in the absence of solvents. The reaction kinetics in a slurry reactor was experimentally studied in order to maximize the yield of benzylamine. Effects of reaction conditions and type of solid catalyst (Raney Ni, Ni/SiO2, Ru/C and Ru/Al2O3) on the content of undesirable by-products were investigated. In particular, benzyl alcohol and trimers of benzylimine: hydrobenzamide and 2,4,5-triphenyl-4,5-dihydro-1H-imidazole were significant by-products found. The formation of poorly soluble high-boiling trimers of benzylimine was minimized by semi-batch arrangement of the process with continuous addition of benzaldehyde into the load of benzylamine, ammonia and Raney-Ni catalyst. Some significant features of benzaldehyde amination, which make it less advantageous for benzylamine production compared to the benzonitrile hydrogenation, are shown.
Over the past two decades, microreaction technology has matured from early devices and concepts to encompass a wide range of commercial equipment and applications. This evolution has been aided by the confluence of microreactor development and adoption of continuous flow technology in organic chemistry. This Perspective summarizes the current state-of-the art with focus on enabling technologies for reaction and separation equipment. Automation and optimization are highlighted as promising applications of microreactor technology. The move towards continuous processing in pharmaceutical manufacturing underscores increasing industrial interest in the technology. As an example, end-to-end fabrication of pharmaceuticals in a compact reconfigurable system illustrates the development of on-demand manufacturing units based on microreactors. The final section provides an outlook for the technology, including implementation challenges and integration with computational tools. AIChE J, 2017 © 2016 American Institute of Chemical Engineers AIChE J, 63: 858–869, 2017.
How fast are flashes? The field of flow chemistry has recently received increasing attention owing to the availability of commercial flow equipment. New syntheses with very short-lived intermediates have been enabled by sub-millisecond mixing and reaction regimes in tailor-made flow devices.
The pharmaceutical industry is investing in continuous flow and high-throughput experimentation as tools for rapid process development accelerated scale-up. Coupled with automation, these technologies offer the potential for comprehensive reaction characterization and optimization, but with the cost of conducting exhaustive multifactor screens. Automated feedback in flow offers researchers an alternative strategy for efficient characterization of reactions based on the use of continuous technology to control chemical reaction conditions and optimize in lieu of screening. Optimization with feedback allows experiments to be conducted where the most information can be gained from the chemistry, enabling product yields to be maximized and kinetic models to be generated while the total number of experiments is minimized.
Over the last two decades, flow technologies have become increasingly popular in the field of organic chemistry, offering solutions for engineering and/or chemical problems. Flow reactors enhance the mass and heat transfer, resulting in rapid reaction mixing, and enable a precise control over the reaction parameters, increasing the overall process selectivity, efficiency and safety. These features allow chemists to tackle unexploited challenges in their work, with the ultimate objective making chemistry more accessible for laboratory and industrial applications, avoiding the need to store and handle toxic, reactive and explosive reagents. This review covers some of the latest and most relevant developments in the field of continuous flow chemistry with the focus on hazardous reactions.
The employment of continuous-flow platforms for synthetic chemistry is becoming increasingly popular in research and industrial environments. Integrating analytics in-line enables obtaining a large amount of information in real-time about the reaction progress, catalytic activity and stability, etc. Furthermore, it is possible to influence the reaction progress and selectivity via manual or automated feedback optimisation, thus constituting a dial-a-molecule approach employing digital synthesis. This contribution gives an overview of the most significant contributions in the field to date.
An automated continuous reactor for the synthesis of organic compounds, which uses online mass spectrometry (MS) for reaction monitoring and product quantification, is presented. Quantitative and rapid MS monitoring was developed and calibrated using HPLC. The amidation of methyl nicotinate with aqueous MeNH2 was optimised using design of experiments and a self-optimisation algorithm approach to produce >93% yield.
Conversion and temperature distributions in a tubular reactor with an inner diameter of 1. mm were numerically calculated for a second order exothermic reaction in laminar flow of homogeneous liquid. Based on the resulting radial temperature profiles, the local Nusselt numbers and bulk mean temperatures were determined along the reactor tube. Strong effects of the homogeneous reaction on the heat transfer can be observed in the entrance region of the reactor, where a hot spot emerges. Due to the large radial temperature gradients in vicinity of the reactor wall, heat transfer coefficients are significantly higher compared to a non-reactive system.The consequences of this effect on the design and control of exothermic reactions in reactor/heat exchangers are demonstrated by comparison with a simple one-dimensional plug flow model. In the simplified model, neglecting thermal influence of the exothermic reaction results in a significant underestimation of the required reactor length for defined conversion. Accordingly, numerical simulation of both axial and radial transport in the hot spot region can be essential to precisely predict the bulk temperatures and conversion rates in the reaction mixture, even with the small length scales of milli- and micro-structured reactors.
In this review the recent progress in the field of self-optimizing reactor systems for continuous flow chemistry is presented. Particular focus is directed to the implementation of monitoring tools and realization of computer-controlled reaction optimizations without human interaction.
We have developed a modular software system that enables researchers to monitor and control chemical reactions via the internet, using any device from any location in the world. It facilitates the automation of synthetic procedures and is able to autonomously self-optimize reaction parameters to find the best conditions meeting customizable, multi-component optimization functions. In this report, we demonstrate its utility as applied to reaction automation to maximize the output from a fixed volume of catalyst. We also showcase its ability to optimize a three dimension heterogeneous catalytic reaction and a five dimension Appel reaction against various target functions.
A design of experiments (DoE) analysis of a tandem SnAr-amidation cyclization reaction between 4-chloropyrimidin-5-amine and (S)-N-methylalanine to form (S)-7,8-dimethyl-7,8-dihydropteridin-6(5H)-one is reported. Five reaction variables were optimized using DoE and conversion was improved from 26% to 74%, with a significant reduction in reaction time while retaining high optical purity. The optimized conditions were applied to the synthesis of a wide variety of analogs and the expanded reaction substrate scope included a variety of amino acids and pyrimidines. Products were obtained in isolated yields up to 95% and enantiomeric excess as high as 98%.
An automated, continuous flow droplet screening system is presented, enabling real-time simultaneous solvent and continuous variable optimization. An optimal design of experiments strategy is applied to the alkylation of 1,2-diaminocyclohexane in 16 μL droplets, with scale-up demonstrated. Analysis of segmented flow results suggests correlation of yield with solvent hydrogen bond basicity.
In this Review we describe how the advent of machines is impacting on organic synthesis programs, with particular emphasis on the practical issues associated with the design of chemical reactors. In the rapidly changing, multivariant environment of the research laboratory, equipment needs to be modular to accommodate high and low temperatures and pressures, enzymes, multiphase systems, slurries, gases, and organometallic compounds. Additional technologies have been developed to facilitate more specialized reaction techniques such as electrochemical and photochemical methods. All of these areas create both opportunities and challenges during adoption as enabling technologies.
© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
In the past few years, continuous-flow reactors with channel dimensions in the micro- or millimeter region have found widespread application in organic synthesis. The characteristic properties of these reactors are their exceptionally fast heat and mass transfer. In microstructured devices of this type, virtually instantaneous mixing can be achieved for all but the fastest reactions. Similarly, the accumulation of heat, formation of hot spots, and dangers of thermal runaways can be prevented. As a result of the small reactor volumes, the overall safety of the process is significantly improved, even when harsh reaction conditions are used. Thus, microreactor technology offers a unique way to perform ultrafast, exothermic reactions, and allows the execution of reactions which proceed via highly unstable or even explosive intermediates. This Review discusses recent literature examples of continuous-flow organic synthesis where hazardous reactions or extreme process windows have been employed, with a focus on applications of relevance to the preparation of pharmaceuticals.
© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Stereoselective Michael addition of 2-(pentan-3-yloxy)acetaldehyde to N-[(Z)-2-nitroethenyl]acetamide is a key step in the organocatalytic synthesis of oseltamivir, active ingredient in the anti-influenza drug Tamiflu. Several important reaction parameters were analyzed by the help of design of experiments and optimum reaction conditions were found. These conditions led to improvements of the reaction outcomes.
Graphical abstract
Statistical design of experiments (DoE) is a powerful tool for optimizing processes, and it has been used in many stages of API development. This review summarizes selected publications from Organic Process Research & Development using DoE to show how processes can be optimized efficiently and how DoE findings may be applied to scale-up.
Green chemistry and flow chemistry are ideal partners for accessing novel chemical spaces and define highly efficient synthetic tools. In this review article contributions have been selected according to the advantages offered in terms of features that are not immediately related to classic green metrics such as minimization of reaction time, optimization for time screening, waste minimization, safety improvement, process intensification and easy scale up, energy and cost efficiency. Such features make processes in flow highly interesting in terms of developing a green and sustainable chemistry.
Ein Forschertraum: schnelle Resultate, die neue Entscheidungen ermöglichen. Die Kombination aus Mikroreaktortechnologie und Online-Analyse ermöglicht nun sogar, Entscheidungen dem Computer zu übertragen und so eine noch schnellere Optimierung von Reaktionsbedingungen vorzunehmen.
Laser-induced Raman spectroscopy has been utilized to demonstrate its feasibility for studying the kinetics of imine formation in chloroform solvent. The imine formation, by the nucleophilic addition of primary amine to the carbonyl group of ketone, has been monitored at ten minute intervals for eight hours. The intensity of the C=O stretching mode at 1684 was measured to determine the rate constant of the reaction. In order to correct the sample-to-sample fluctuations in Raman peak area, this peak was normalized to the C-Cl bending peak at 666 . By the peak area change during the course of reaction, the second order rates at three different temperatures have been determined. The substituent effects on the π conjugations of imine product have also been investigated. On the basis of Raman frequency shifts, the delocalization properties of the aromatic system modified by substitution of a hydrogen atom with -Cl and groups could be clearly understood.
Reductive debenzylation of hexabenzylhexaazaisowurtzitane (HBIW) was carried out using palladium hydroxide deposited on activated carbon catalyst. The catalyst has been characterized using a nitrogen adsorption/desorption isotherm, a hydrogen isotherm, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The synthesized catalyst showed pore sizes larger than 20 Å, which are attributed to mesopore structures. A multivariate optimization approach was developed by means of central composite design (CCD) for optimizing reaction conditions. The influence of four variables, including the catalyst to HBIW (1) relative percent, reaction temperature, hydrogen pressure, and acetic anhydride (Ac2O), on the reaction yield was investigated. The results showed that the optimum parameters for reductive debenzylation were 20% (w/w) for catalyst to HBIW (1) percent, 48.5 °C for reaction temperature, 4.25 bar for hydrogen pressure, and 10.9 for Ac2O/HBIW mole ratio. Under these conditions, the reaction yield increased to 73%.
Automated continuous flow systems coupled with online analysis and feedback have been previously demonstrated to model and optimize chemical syntheses with little a priori reaction information. However, these methods have yet to address the challenge of modeling and optimizing for product yield or selectivity in a multistep reaction network, where low selectivity toward desired product formation can be encountered. Here we demonstrate an automated system capable of rapidly estimating accurate kinetic parameters for a given reaction network using maximum likelihood estimation and a D-optimal design of experiments. The network studied is the series–parallel nucleophilic aromatic substitution of morpholine onto 2,4-dichloropyrimidine. To improve the precision of the estimated parameters, we demonstrate the use of the automated platform first in optimization of the yield of the less kinetically favorable 2-substituted product. Then, upon isolation of the intermediates, we use the automated system with maximum a posteriori estimation to minimize uncertainties in the network parameters. From considering the steps of the reaction network in isolation, the kinetic parameter uncertainties are reduced by 50%, with less than 5 g of the dichloropyrimidine substrate consumed over all experiments. We conclude that isolating pathways in the multistep reaction network is important to minimizing uncertainty for low sensitivity rate parameters.
A multijet oscillating disk (MJOD) millireactor system suitable for operating at cryogenic temperatures has been developed, assembled, and investigated. This new reactor system (cryoMJOD) was realized with the purpose to prepare various phenylboronic acids in a continuous two (three)-step telescoped synthetic process at temperatures in the interval −50 to −75 °C. In this process, n-butyllithium was reacted with a phenylbromide to provide the corresponding phenyllithium derivative, whereupon a borate was added under the formation of target product phenylboronic acid in good selectivity and medium-to-good yield (50–75%). These results were competitive with results previously revealed in the literature. The residence times of the telescoped two-step process were considerably shorter compared to those of batch mode operations for the identical syntheses. The flow process was optimized by means of statistical experimental design and multivariate regression, upon which the process was utilized for the production of a series of phenylboronic acid derivatives, all in medium-to-good yield. One of the substrates, 4-methoxyphenylboronic acid, was submitted for throughput improvements, resulting in a process with capability to produce the product phenylboronic acid in a quantity of 2.0 kg × day–1.
The allylic hydroxylation of enones using dioxygen as the oxidant has been studied. The reaction was first examined in the absence of any catalyst, using β‐ionone as a model substrate. Then a new copper–aluminium mixed oxide, Cu–Al Ox, was prepared and characterized in order to be used as a catalyst. This oxide showed good activity, and provided the corresponding γ‐ or ‐hydroxylated enones, starting from different α,β‐ or α,β,γ,δ‐unsaturated ketones. In all cases, the yields were significantly improved compared to experiments run in the absence of the catalyst. The reaction was selective, and the formation of epoxides or other overoxidation products was detected only to a minor extent. The described procedure is a technically straightforward synthetic alternative to those methods described to date involving many reaction steps or toxic reagents. The reactions were optimized using design of experiments techniques (DoE).
This review provides an overview of recent developments in the area of continuous flow process optimization by employing self-optimizing reactor systems. Although only few reactor concepts have been realized to date, impressive progress has been made, which now allows fully automated reaction optimization without the need of human interactions.
The use of automated continuous flow reactors is described, with real-time online Fourier transform infrared spectroscopy (FT-IR) analysis to enable rapid optimization of reaction yield using a self-optimizing feedback algorithm. This technique has been applied to the solvent-free methylation of 1-pentanol with dimethyl carbonate using a γ-alumina catalyst. Calibration of the FT-IR signal was performed using gas chromatography to enable quantification of yield over a wide variety of flow rates and temperatures. The use of FT-IR as a real-time analytical technique resulted in an order of magnitude reduction in the time and materials required compared to previous studies. This permitted a wide exploration of the parameter space to provide process understanding and validation of the optimization algorithms.