Multi objective optimization and inverse product specification at an early stage of conceptional process design

Multi-objective optimization and inverse product specification in conceptional process designJ. Burger, H. HasseTechnical University Kaiserslautern, Laboratory of Engineering Thermodynamics, Germany.

Multi-objective optimization (MOO) is recognized as powerful tool in chemical engineering[1]. Recent research focusses also on the use of MOO in conceptional process design when several process variants are compared[2]. By calculation of Pareto optimal sets in the objective space, the decision maker does not have to choose weights for the objectives before the optimization. By considering all Pareto optimal alternatives globally the decision maker maintains the decision flexibility and there is no loss of optimal solutions. In this contribution MOO based on Pareto optimal sets is applied to the design of an industrial process for yielding poly(oxymethylene) dimethyl ethers (OMEs), which are highly interesting environmentally friendly fuel additives. OMEs are oligomers of the general structure CH3O(CH2O)nCH3 and feature soot reduction during the engine combustion when they are added to diesel fuel[3]. They can be produced on large scale from the sole feedstock methane which is available as either natural or bio gas[4]. The OME process chain is, hence, a new option for gas-to-liquid technology. In the final step of the process chain, which is studied in detail in the present work, methylal and trioxane are converted into OMEs. For this final OME process step many specifications are still not known. Among them there are the feed composition and the product specification: the optimal OME blend of different chain lengths which shall be used for fuel blending. The present work describes a rational way to find the optimal blend composition from a process engineering standpoint, i.e. we address inverse product specification.

To achieve this, a reduced process model is used which is based on /-analysis[5]. From this model new quantitative objectives are derived which represent costs for the reactor as well as the downstream separation. Calculations of Pareto optimal sets are used to optimize all process parameters without fixing the missing specifications. Conclusions drawn from the results of the optimization lead to the feed composition and finally to the optimal product blend, so that the problem of inverse product specification is solved. After the missing specifications are found the optimized process can be simulated in detail and the equipment can be designed.Pareto optimization or MOO is a powerful tool for flexible decision making and can be used in an early stage of conceptional design. Especially when some specifications are missing, the design process can continue without constraining or losing promising alternatives. This saves time and provides more flexibility and assurance to the decision maker. [1]V. Bhaskar et al.: Applications of multiobjective optimization in chemical engineering, Reviews in Chemical Engineering 16, 2000, 154[2]N. Asprion et al.: Decision support for process development in the chemical industry, Chemical Engineering Transactions 24, 2011, 301306.[3]B. Lumpp et al.: Oxymethylene ethers as diesel fuel additives of the future, MTZ worldwide 72, 2011, 3438[4]J. Burger et al.: Poly(oxymethylene) dimethyl ethers as components of tailored diesel fuel: Properties, synthesis and purification concepts, Fuel 89, 2010, 33153319

[5]N. Bekiaris, M. Morari: Multiple steady states in distillation: / predictions, extensions, and implications for design, synthesis, and simulation, Ind. Eng. Chem. Res. 35, 1996, 42644280