Cyclic operation of a semi-batch reactor for the hydroformylation of long-chain olefins and integration in a continuous production process

https://doi.org/10.1016/j.cej.2018.11.151Get rights and content

Highlights

  • Manual for modeling a cyclic semi-batch reactor in a continuous process.

  • Simulations indicate a prolonged start-up time for the hydroformylation process.

  • Experimental prove of concept of a cyclic hydroformylation process in a TMS-system.

Abstract

The transition to renewable feedstocks in chemicals production requires innovative processes which are able to exploit the special properties of the feed material while still maintaining high process performance. Flexible semi-batch processes offer the advantage of dynamic adaptation to changing process requirements but suffer in terms of automation and production capacity where continuous processes excel. Combining the flexibility of a semi-batch reactor with the reliability of a continuous downstream process is the motivation for the application of a repeatedly operated semi-batch reactor (RSBR) concept to the hydroformylation of 1-dodecene in a n-decane/DMF thermomorphic multiphase system (TMS). In order to predict the dynamic process behavior, a detailed dynamic process model is introduced and compared to a corresponding steady-state model. In addition, the RSBR concept is embedded in a miniplant process to prove its feasibility and convergence to a cyclic steady-state experimentally. Finally, the collected experimental data is compared to the results from the dynamic process model indicating accurate predictions of the integral process behavior.

Introduction

The production of chemicals can generally be divided into two major categories: commodity (bulk) and fine (specialty) chemical production. They are differentiated due to varying requirements with respect to process capacity, flexibility, product quality as well as applicability of automation. The product demand for fine chemicals is normally moderate, allowing for smaller production scales, an area fit for batch processes. Additionally, to lower investment costs and compact construction, batchwise process operation allows for close monitoring of the product quality as well as flexible and adaptive process operation. Fluctuations in the substrate composition or impurities in the feed stream can be counteracted by exploiting the additional degrees of freedom provided. Continuous processes, on the other hand, excel in production capacity and do not suffer from preparation times, leading to favorable applications in the synthesis of bulk, especially basic, chemicals. In light of transitioning from petroleum based to renewable feedstocks for chemical production, process flexibility is of great importance to handle the varying quality of raw materials. Therefore, combining the flexibility of batchwise operation with the throughput and automation of continuously operated plants should be aspired.

The focus of the present work lies on the recently introduced reactor concept of a repeated semi-batch reactor (RSBR) integrated in a continuous process for the production of n-tridecanal (nC13al) from 1-dodecene (1C12en) over hydroformylation. Kaiser et al. [1] used the methodology of Elementary Process Functions (EPF) [2] combined with the Flux Profile Analysis (FPA) approach [3] to identify optimal reactor-networks for the hydroformylation in a thermomorphic multiphase system (TMS) [4]. It was shown that integrating a semi-batch reactor (SBR) into the overall process via buffer tanks results in nearly identical performance as conventional continuous processes in terms of conversion and selectivity towards the desired product but with the additional benefit of the flexibility of a SBR. To further investigate the benefits and possible limitations of this quasi-continuous process operation, a dynamic (Dyn) model of the hydroformylation process using the RSBR and the process setup presented by Dreimann et al. [5] is developed and compared to the results of a steady-state (SS) process model as well as experimental results from a reduced process setup.

The paper is organized as follows. At first, the configuration of the considered process is introduced in Section 3 after providing background information on the hydroformylation and discussing some previous work in this area in Section 2. Afterwards, a steady-state model is introduced in Section 4.2 which serves as a basis for comparison to the dynamic process model presented in Section 4.3. Section 5 introduces the experimental setup and summarizes the considered process conditions. In Section 6, the steady-state model is compared to the newly introduced dynamic process model. Finally, the article concludes with some final remarks in Section 7.

Section snippets

Background

The present work is part of the Collaborative Research Center TR 63 Integrated Chemical Processes in Liquid Multiphase Systems, InPROMPT of the German Research Foundation, a transregional cooperation with the goal to reduce the time-to-market of innovative and efficient processes. The process design is carried out with the use of renewable raw materials in mind, providing alternatives to conventional processes. In this collaboration, the major focus lies on the hydroformylation: a homogeneously

Process configuration

For better comparability, the hydroformylation process described by Kaiser et al. [1] is used as an example process. In Fig. 2, the process configuration is depicted. It consists of a SBR, integrated in the continuous process via two buffer tanks – a feed buffer tank and a flash buffer tank. Following the flash buffer tank (DBuffer), a CSTR is present as a secondary reaction zone due to its beneficial back-mixing characteristics for this reaction [1]. Afterwards, the reaction mixture enters a

Process models

For the in-depth analysis of the RSBR-process, two process models are introduced – a steady-state and a dynamic model. Both of them share common process unit models which are summarized in Section 4.1. Afterwards, distinct features of the steady-state model as well as the dynamic model are discussed in Sections 4.2 Steady-state model, 4.3 Dynamic process model, respectively. For brevity, the reaction rate, constitutive and model equations of the different process units can be found in Section A

Experimental setup

For testing the feasibility of the quasi-continuous process operation and the predictions made by the steady-state and dynamic process model, experiments are prepared under miniplant conditions. A reduced process setup, depicted in Fig. 5, is employed neglecting the apolar recycle from the distillation column (compare Fig. 2). Table 2, Table 3 provide a list of equipment and chemicals, respectively, which were used in the miniplant setup and Table 4 summarizes the experimental conditions.

Every

Results and discussion

In the following, simulation results from both process models as well as the experimental results are discussed. The remainder of this section is organized as follows. After validation of the steady-state process model (see Section C of the supplementary materials), the dynamic process model is compared to the steady-state model to verify its integrity and to show additional aspects of the process which are not described by the steady-state model. Subsequently, the dynamic model is used to

Conclusion

Based on the process design by Kaiser et al. [1] for the hydroformylation of 1-dodecene in a n-decane/DMF TMS, a dynamic process model has been developed. By dividing the process into two stages and performing separate cycle-based optimizations of each stage sequentially, a repeated semi-batch process is simulated which allows for the investigation of non-stationary process states, e.g., process start-up.

For the validation of the dynamic process model, a steady-state model has been formulated

Acknowledgment

This work is part of the Collaborative Research Center/Transregio 63 “Integrated Chemical Processes in Liquid Multiphase Systems” (subproject B1). Financial support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) is gratefully acknowledged (TRR 63). The author Karsten H. G. Rätze is also affiliated to the “International Max Planck Research School (IMPRS) for Advanced Methods in Process and Systems Engineering (Magdeburg)”. Furthermore, the authors would like to thank

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