Process Optimization Continued

Session Chair:

• Armin Fricke, Chemstations Europe 

Session Description:

Process Optimization is undergoing a paradigm shift. In the past, good knowledge about the process, its components, and clear objectives for the optimization were a sufficient starting point for working with a process simulator. Today, process optimization comes from many different directions: Modular plants, autonomous plants, smart maintenance, surrogate modeling, metaheuristics, and cluster computing.

New enablers for process optimization are digitization, which results in the Digital Twin and collaborative workflows, and machine learning, which allows to build gray-box models combining rigorous thermodynamic simulation and plant data.

This session focuses on the methods and tools available for process optimization.

Confirmed Talks:

Abstracts:

Process Design and Optimization for the Hydroformylation of Long-chain Alkenes

Kevin McBride, Research Associate, Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems

K. McBride1, C. Kunde2, S. Linke2, T. Keßler2, K. Rätze2, M. Jokiel1, A. Kienle1,2,

K. Sundmacher1,2

1 Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, 39106 Magdeburg, Germany

2 Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany

The substitution of current feedstock with renewable resources is a major challenge in today’s chemical process industries. To focus more specifically on this research area, the Collaborative Research Center SFB/TR63 – Integrated Chemical Processes in Liquid Multiphase Systems (InPROMPT) of the Deutsche Forschungsgemeinschaft (DFG) was conceived. The main emphasis of this collaboration is the development of process structures for homogeneously catalyzed reactions in liquid multiphase systems.

As part of this effort, we primarily focus on the development of optimal reactor concepts for liquid multiphase systems, in particular for the rhodium-catalyzed hydroformylation of long-chain olefins. The main challenge of this process is the quantitative recovery of the rhodium catalyst and equally important ligands. To this end, thermomorphic solvent systems (TMS) are used which allow for temperature induced phase splitting in the post-reaction mixture [1]. The catalyst rich phase is recycled to the reactor and the product rich phase is treated further downstream. Applying a multi-scale design approach, the molecular design of the solvent system directly influences all other process units. This leads to an overall process design task that combines both the solvent and reactor optimizations as part of an integrated process optimization problem.

This is no simple task and this presentation focuses on our recent achievements and methods used towards reaching this goal. As an expansion to the single-stage separation TMS, an extraction cascade was proposed to further reduce catalyst leaching and increase the domain of acceptable TMS solvents [2]. This prompted the development of several strategies for TMS solvent design [3] with an emphasis on not only process performance but also considering ecological characteristics [4]. Frequent liquid-liquid equilibrium (LLE) calculations during optimization are numerically challenging and in order to simplify the optimization of the resulting process flowsheets, several different surrogate models representing the LLE and catalyst partitioning were implemented [2,5]. On the reaction side, The use of the EPF methodology [6] allows us to identify reactor concepts, such as a cyclic semi-batch or helically coiled tubular reactor, that lead to better reaction performance than when using conventional arrangements [7,8]. The various tools and strategies being devised in this project will hopefully enable us to develop new processes based around complex homogeneously catalyzed multiphase reactions, such as for the hydroformylation of long-chain olefins, as part of a single, integrated design step.

References

[1]  A. Behr and C. Fängewisch, 2002, Temperature-dependent multicomponent solvent systems-an alternative concept for recycling homogeneous catalysts, Chem. Eng. Technol., 25, pp. 143-147

[2]  K. McBride, N.M. Kaiser, K. Sundmacher, 2017, Integrated reaction-extraction process for  the hydroformylation of long-chain alkenes with a homogeneous catalyst, Comput. Chem. Eng., 105, pp. 212-223

[3]  K. McBride, T. Gaide, A. Vorholt, A. Behr, K. Sundmacher, 2016, Thermomorphic solvent selection for homogeneous catalyst recovery based on COSMO-RS, Chem. Eng. Process., 99, pp. 97-106

[4]  K. McBride, S. Linke, S. Xu, and K. Sundmacher, 2018, Computer Aided Design of Green Thermomorphic Solvent Systems for Homogeneous Catalyst Recovery. Comput. Aided Chem. Eng., 44, pp. 1783-1788.

[5]  T. Keßler, C. Kunde, K. McBride, N. Mertens, D. Michaels, K. Sundmacher, A. Kienle, 2019, Global optimization of distillation columns using explicit and implicit surrogate models. Chem. Eng. Sci., 197, pp. 235-245

[6]  A. Peschel, H. Freund, K. Sundmacher, 2010, Methodology for the Design of Optimal Chemical Reactors Based on the Concept of Elementary Process Functions, Ind. Eng. Chem. Res., 49, pp. 10535–10548

[7]  K. Rätze, M. Jokiel, N. Kaiser, K. Sundmacher, 2018, Cyclic operation of a semi-batch reactor for the hydroformylation of long-chain olefins and integration in a continuous production process, Chem. Eng. J., in press, DOI: 10.1016/j.cej.2018.11.151

[8] M. Jokiel, N. Kaiser, P. Kováts, M. Mansour, K. Zähringer, K. D. P. Nigam, K. Sundmacher, 2018, Helically coiled segmented flow tubular reactor for the hydroformylation of long-chain olefins in a thermomorphic multiphase system, Chem. Eng. J., in press, DOI: 10.1016/j.cej.2018.09.221

Agrochemical Process Research: Searching For The Holistic Solution

Dirk Brohm, Lab Leader High Pressure Lab, Bayer

Using examples from Process research and the industrial production of active ingredients some of the thinking beyond the development of an agrochemical technical process will be discussed. Emphasis will be placed on issues which really drive the technical feasibility of large scale AI production at a cost that is applicable for an agrochemical. Furthermore, an outlook for new trends in process optimization will be given. 

Analysis and Optimization of Complex Flowsheet Simulations with Parallel Computing and Machine Learning

Jan Schöneberger and Armin Fricke, Chemstations Europe

Convergence problems are common when dealing with complex flowsheet simulations. These usually do not result from shortcomings of the numerical algorithms, but from an unfavorable or even impossible setting of the degrees of freedom for the underlying system of equations.

Due to the highly non-linear relationships of the variables of state, users of simulation software often cannot identify which design variables in which range or combination lead to unsolvable systems of equations. This leads to time-intensive trial-and-error simulation runs in practice.

Multivariate sensitivity studies (MSS) can be used to systematically and automatically analyze complex flowsheets in the multidimensional space of the design variables. Parallelization of the calculation of flowsheets drastically reduces the time required.

Machine learning methods can be applied, for instance, to identify and exclude areas where the flow diagram does not converge before the calculation. This artificial intelligence is used to control optimization algorithms for the evaluation of complex flow diagram simulations

This approach is demonstrated using two examples from the field of distillation. Figure 1 shows the results space of an MSS for a rectification column for the separation of ethanol and water.

Figure 1: Result of the MSS for a simple flowsheet with a column with 20 equilibrium stages RD: reflux ratio (design variable); VB: boil-up ratio (design variable); zEtOH: mass fraction of ethanol in the distillate; mD: distillate mass flow; QR: energy requirement of the reboiler.