Statistical analysis and optimization for direct dimethyl ether synthesis from syngas over CuO–ZnO–Al2O3/γ-Al2O3 bifunctional catalysts

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Abstract Direct synthesis of dimethyl ether has been statistically analyzed in terms of product distribution and the effect of operating conditions. The investigated catalyst system consists of a combination of methanol synthesis (CuO–ZnO–Al2O3) and methanol dehydration (γ-Al2O3). The range of operating conditions varied as: T = 200–260 0C, H2/CO = 0.67-2 and SV (Space Velocity) = 41.74-292.68 h− 1 at P = 5.1 MPa. Using data obtained from a fixed bed microreactor, the product selectivity models were developed as functions of the above parameters via response surface methodology. The models were efficiently adjusted to avoid overfitting by considering cross-validation. The effects are shown via 3D diagrams. Single and multi-objective optimizations were then employed to maximize the production of DME and CO conversion and minimize the production of methanol, hydrocarbons, and carbon dioxide. Numerical optimization was performed through desirability charge ranging from zero to one where the highest desirability satisfies optimum conditions. Accordingly, an optimum area was obtained in which a variety of different points can be acceptable. Single-objective optimization provides a vaster area while multi-objective limits the feasible process conditions range.
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Statistical analysis and optimization for direct dimethyl ether synthesis from syngas over CuO–ZnO–Al2O3/γ-Al2O3 bifunctional catalysts | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Statistical analysis and optimization for direct dimethyl ether synthesis from syngas over CuO–ZnO–Al2O3/γ-Al2O3 bifunctional catalysts Amin Einbeigi, Mahdi Khorashadizadeh, Hossein Atashi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4406477/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Direct synthesis of dimethyl ether has been statistically analyzed in terms of product distribution and the effect of operating conditions. The investigated catalyst system consists of a combination of methanol synthesis (CuO–ZnO–Al 2 O 3 ) and methanol dehydration (γ-Al 2 O 3 ). The range of operating conditions varied as: T = 200–260 0 C, H 2 /CO = 0.67-2 and SV (Space Velocity) = 41.74-292.68 h − 1 at P = 5.1 MPa. Using data obtained from a fixed bed microreactor, the product selectivity models were developed as functions of the above parameters via response surface methodology. The models were efficiently adjusted to avoid overfitting by considering cross-validation. The effects are shown via 3D diagrams. Single and multi-objective optimizations were then employed to maximize the production of DME and CO conversion and minimize the production of methanol, hydrocarbons, and carbon dioxide. Numerical optimization was performed through desirability charge ranging from zero to one where the highest desirability satisfies optimum conditions. Accordingly, an optimum area was obtained in which a variety of different points can be acceptable. Single-objective optimization provides a vaster area while multi-objective limits the feasible process conditions range. Dimethyl Ether Syngas Response surface methodology Selectivity modeling Optimization Full Text Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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