Integrated reaction–extraction process for the hydroformylation of long-chain alkenes with a homogeneous catalyst

Abstract

A lingering issue with the hydroformylation of long-chain alkenes is the cost of catalyst leaching. One effective method to recover homogeneous catalysts is the use of thermomorphic solvent systems (TMS). However, catalyst leaching is still too high using the current solvents DMF and decane, limiting economic feasibility. This work presents extraction as a possible method for intensifying catalyst recovery when using a TMS for the hydroformylation of 1-dodecene. A thermodynamic model for determining the LLE of the solvent system and for catalyst leaching is developed for implementation within a process-wide optimization problem. Using this model, the optimal reactor design with an integrated downstream separation including the catalyst loss can be investigated in more detail. It is shown that in this process the reactor design strongly depends on catalyst recovery and that by using the proposed extraction cascade the process becomes economically viable and more robust in regards to reactor performance.

Overview of Surrogate Modeling in Chemical Process Engineering

“…To avoid calculating the liquid‐liquid equilibrium during the optimization of a process for the hydroformylation of 1‐dodecene, McBride et al. replaced the decanter in the flow sheet with a kriging surrogate. Kaiser et al.…”

“…Modular surrogate modeling has been used for thermodynamic equilibrium, which often leads to convergence issues during optimization when solved implicitly. To avoid calculating the liquid-liquid equilibrium during the optimization of a process for the hydroformylation of 1-dodecene, McBride et al [60] replaced the decanter in the flow sheet with a kriging surrogate. Kaiser et al [61] then used this same model for optimal reactor design in a similar process.…”

Surrogate Modeling for Liquid–Liquid Equilibria Using a Parameterization of the Binodal Curve

“…Applications for surrogate modeling include, but are not limited to, cases where the original function is expensive to evaluate, e.g., computational fluid dynamics simulations, or where a functional form may not be available, e.g., experimental results. This section gives a summary of recent efforts [4,10] on using data-driven surrogate models for phase equilibrium calculations, particularly for LLE, to improve computational performance while preserving high model accuracy. For the sake of readability, some details of each approach are omitted here and we refer to the original publications for comprehensive descriptions.…”

“…In the model comprising Equations (7)-(10), f i is replaced by a surrogatef i ≈ f i . Kriging interpolation is used for the surrogatesf i in [4,10]. The inputs of the surrogate are N c − 1 mole fractions of the feed mixture and the temperature.…”

“…The selection of sample locations for data generation can be categorized in one-shot space-filling designs, which select locations according to pre-defined strategies, and adaptive sampling approaches, which exploit information from available sampling data to select new sampling locations. In [10], sample values are calculated by evaluating the equilibrium model at pre-defined sample locations generated by a space-filling design. A major contribution of Nentwich and Engell [4] is a method of adaptively choosing new sample locations based on available sample data to improve the quality of the approximation compared to one-shot designs with the same number of samples.…”