The aim of this work is to develop group-contribution(+) (GC(+)) method (combined group-contribution (GC) method and atom connectivity index (CI) method) based property models to provide reliable estimations of environment-related properties of organic chemicals together with uncertainties of estimated property values. For this purpose, a systematic methodology for property modeling and uncertainty analysis is used. The methodology includes a parameter estimation step to determine parameters of property models and an uncertainty analysis step to establish statistical information about the quality of parameter estimation, such as the parameter covariance, the standard errors in predicted properties, and the confidence intervals. For parameter estimation, large data sets of experimentally measured property values of a wide range of chemicals (hydrocarbons, oxygenated chemicals, nitrogenated chemicals, poly functional chemicals, etc.) taken from the database of the US Environmental Protection Agency (EPA) and from the database of USEtox is used. For property modeling and uncertainty analysis, the Marrero and Gani GC method and atom connectivity index method have been considered. In total, 22 environment-related properties, which include the fathead minnow 96-h LC(50), Daphnia magna 48-h LC(50), oral rat LD(50), aqueous solubility, bioconcentration factor, permissible exposure limit (OSHA-TWA), photochemical oxidation potential, global warming potential, ozone depletion potential, acidification potential, emission to urban air (carcinogenic and noncarcinogenic), emission to continental rural air (carcinogenic and noncarcinogenic), emission to continental fresh water (carcinogenic and noncarcinogenic), emission to continental seawater (carcinogenic and noncarcinogenic), emission to continental natural soil (carcinogenic and noncarcinogenic), and emission to continental agricultural soil (carcinogenic and noncarcinogenic) have been modeled and analyzed. The application of the developed property models for the estimation of environment-related properties and uncertainties of the estimated property values is highlighted through an illustrative example. The developed property models provide reliable estimates of environment-related properties needed to perform process synthesis, design, and analysis of sustainable chemical processes and allow one to evaluate the effect of uncertainties of estimated property values on the calculated performance of processes giving useful insights into quality and reliability of the design of sustainable processes.

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Synthetic diesel fuel produced from natural gas via gas-to-liquid (GTL) technology is referred to as ultraclean fuel but is still challenged for full certification as diesel fuel. GTL diesel lacks certain hydrocarbons and chemical constituents, which although are benign to the environment, result in a trade-off in performance when used in a diesel engine. To boost GTL diesel physicochemical properties and thereby enable its use in conventional diesel engines, GTL diesel needs improvement. This can be achieved by mixing suitable additives to the GTL diesel and through the development of surrogate fuels that have fewer components. Screening of thousands of additives is a tedious task and can be done efficiently via computer based modeling to quickly and reliably identify a small number of promising candidates. These models are used to guide the formulation of five surrogates and predict their physicochemical properties. These surrogates are further verified using rigorous mathematical tools as well as through advanced experimental techniques. An optimal surrogate MI-5 is identified, which closely mimics GTL diesel-conventional diesel blends in terms of its physicochemical properties. An engine study for the surrogate is also performed to understand the effect of physicochemical properties on combustion as well as the emission behavior of the fuel. MI-5 exhibited an optimal torque at higher load conditions. A reduction of 11.26% NOx emission for MI-5 is observed when compared to conventional fuel. At higher loads, diesel fuel surpasses the total hydrocarbon (THC) emissions for both the surrogate and the GTL fuel. No significant variation in CO and CO2 emissions for MI-5, GTL diesel and conventional diesel is observed. Analysis of combustion as well as emission behavior of the fuels helps to understand the role of physicochemical properties on the performance of the fuel.

 Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

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Modern society needs various chemical products for its survival. The chemical products are classified in terms of single species products, multiple species products, and devices. Multiple species products such as mixtures and blends are one of the most widely used chemical products. However, the common design methods for this kind of product are still mostly by trial and error or by rule-based approaches. A computer-aided methodology integrated with experimental verification is presented in this article. In the first step of this methodology, model-based computer-aided techniques are employed to the design of mixtures and blends. In the second step, the properties of the most promising product candidates are verified through experiments and/or rigorous models. The starting point is to analyze the product needs and translate them into target property constraints. A list of molecules that serve as ingredient-chemicals for addition to the blended product together with their pure compound properties are generated using a well-known computer-aided design molecular design technique. A mixed integer nonlinear programming (MINLP) model is established for the selection of the ingredient-chemicals and their compositions in the blended product. The solution methods for the MINLP model are presented. For the first time, phase equilibrium based properties (such as liquid solution activity coefficients) are modeled and solved simultaneously in the MINLP model through the use of UNIFAC model. Finally, the optimization results are verified through experiments and rigorous models. Two application examples highlighting tailor-made surrogate fuel designs of a gasoline blend and a jet-fuel blend are presented.

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