Design and Control Integration of a Reactive Distillation Column for Ethyl Lactate Production

ABSTRACT: Nowadays, the worldwide tendency to obtain environmentally friendly products through the use of safe and stable production processes, minimizing the energy consumption (i.e. using energy integration), and avoiding products out of specification, are an important motivation for applying a process design methodology that incorporates controllability issues since the earliest design stages. Although the topic of design-control integration has been a research topic investigated from different fronts for more than thirty-five years, it was in 2005 where a methodology incorporating local practical controllability issues for nonlinear systems was proposed. Such methodology allows designing processes that fulfill some controllability criteria, which assures that the resulted design will be controllable from the modern control theory. The mentioned design-control integration methodology was applied in this work for designing a reactive distillation column for producing ethyl lactate, an important green solvent. Production of this green solvent has gained great attention worldwide since it is seen as an excellent alternative for replacing petroleum-based solvents. As with any green product that intends to replace oil-based products, ethyl lactate production needs to be improved (in terms of its economic feasibility) to have an actual chance for replacing the petroleum-based solvents at a worldwide scale. One of the proposals for improving the economic feasibility of this green solvent, is to produce it in a reactive distillation column system, which would reduce the energy consumption, increasing the process profit. The design-control methodology applied here involved several steps. First, the development of a first principles-based model is required. Unfortunately, experimental data for a reactive distillation system for ethyl lactate production are scarce. Therefore, the model was identified and validated using data generated by running simulations in Aspen Plus. After model validation, simulated data were used in conjunction with knowledge of the process (obtained from technical literature) to select the state variables to be controlled. Then the manipulated and controlled variables were paired by applying digraphs theory, which avoids linearization of the nonlinear model. After this, local practical controllability metrics were formulated for being used as constraints during the optimization step of the design-control methodology. Besides the controllability metrics, physical constraints as well as product specifications constraints were included in the optimization. To compare the integrated design methodology with a traditional design methodology, the optimization was also run but considering only the physical and product specifications as constraints, but not the controllability metrics. Results of the comparison of the integrated design and the traditional design methodologies have shown that the design obtained by using the design control methodology leads to a higher profit while fulfilling all the constraints. A key factor in the design of the reactive distillation column is the ratio between the number of trays in the rectification zone and the stripping zone. Therefore, the optimization was run for several values of this ratio. Then the best case for this ratio was used for finally designing the column under the design–control methodology. Furthermore, as defining a ratio between the column length and column diameter is a common practice in the traditional design of distillation columns, in this work, such ratio was also included as a constraint in the optimization problem, to investigate how it impacted the optimal design results. It was observed that such type of constraint is not suitable for being included in the design of the reactive distillation column for the analyzed case study

Deep deterministic policy gradient: applications in process control and integrated process design and control

In recent years, the urgent need to develop sustainable processes to fight the negative effects of climate change has gained global attention and has led to the transition into renewable energies. As renewable sources present a complex dynamic behavior, this has motivated a search of new ways to simulate and optimize processes more efficiently. One emerging area that has recently been explored is Reinforcement learning (RL), which has shown promising results for different chemical engineering applications. Although recent studies on RL applied to chemical engineering applications have been performed in different areas such as process design, scheduling, and dynamic optimization, there is a need to explore further these applications to determine their technical feasibility and potential implementation in the chemical and manufacturing sectors. An emerging area of opportunity to consider is biological systems, such as Anaerobic Digestion Systems (AD). These systems are not only able to reduce waste from wastewater, but they can also produce biogas, which is an attractive source of renewable energy. The aim of this work is to test the feasibility of a RL algorithm referred to as Deep Deterministic Policy Gradient (DDPG) to two typical areas of process operations in chemical engineering, i.e., process control and process design and control. Parametric uncertainty and disturbances are considered in both approaches (i.e., process control and integration of process and control design). The motivation in using this algorithm is due to its ability to consider stochastic features, which can be interpreted as plant-model mismatch, which is needed to represent realistic operations of processes. In the first part of this work, the DDPG algorithm is used to seek for open-loop control actions that optimize an AD system treating Tequila vinasses under the effects of parametric uncertainty and disturbances. To provide a further insight, two different AD configurations (i.e., a single-stage and a two-stage system) are considered and compared under different scenarios. The results showed that the proposed methodology was able to learn an optimal policy, i.e., the control actions to minimize the organic content of Tequila in the effluents while producing biogas. However, further improvements are necessary to implement this DDPG-based methodology for online large-scale applications, e.g., reduce the computational costs. The second part of this study focuses on the development of a methodology to address the integration of process design and control for AD systems. The objective is to optimize an economic function with the aim of finding an optimal design while taking into account the controllability of the process. Some key aspects of this methodology are the consideration of stochastic disturbances and the ability to combine time-dependent and time-independent actions in the DDPG. The same two different reactor configurations considered in the optimal control study were explored and compared in this approach. To account for constraints, a penalty function was considered in the formulation of the economic function. The results showed that there are different advantages and limitations for each AD system. The two-stage system required a larger investment in capital costs in exchange of higher amounts of biogas being produced from this design. On the other hand, the single-stage AD system required less investment in capital costs in exchange of producing less biogas and therefore lower profits than the two-stage system. Overall, the DDPG was able to learn new control paths and optimal designs simultaneously thus making it an attractive method to address the integrated design and control of chemical systems subject to stochastic disturbances and parametric uncertainty.

Modelling and Optimization of Conventional and Unconventional Batch Reactive Distillation Processes. Investigation of Different Types Batch Reactive Distillation Columns for the Production of a Number of Esters such as Methyl Lactate, Methyl Decanoate, Ethyl Benzoate, and Benzyl Acetate using gPROMS

The synthesis of a number of alkyl esters such as methyl lactate, methyl decanoate, and ethyl benzoate via esterification in a reactive distillation is quite challenging. It is due to the complexity in the thermodynamic behaviour of the chemical species in the reaction mixture in addition to the difficulty of keeping the reactants together in the reaction section. One of the reactants (in these esterification reactions) having the lowest boiling point can separate from the other reactant as the distillation continues. This can result in a significant drop in the reaction conversion in a conventional reactive distillation whether it is a batch or a continuous column. To overcome this challenge, new different types of batch reactive distillation column configurations: (1) integrated conventional (2) semi-batch (3) integrated semi-batch (4) integrated dividing-wall batch distillation columns have been proposed here. Four esterification reaction schemes such as (a) esterification of lactic acid (b) esterification of decanoic acid (c) esterification of benzoic acid (d) esterification of acetic acid are investigated here. A detailed dynamic model based on mass, energy balances, chemical reaction, and rigorous thermodynamic (chemical and physical) properties is considered and incorporated in the optimisation framework within gPROMS (general PROcess Modelling System) software. It is found that for the methyl lactate system, the i-SBD operation outperforms the classical batch operations (CBD or SBD columns) to satisfy the product constraints. While, for the methyl decanoate system, the i-DWCBD operation outperforms all CBD, DWBD and sr-DWBD configurations by achieving the higher reaction conversion and the maximum product purity. For the ethyl benzoate system, the performance of i-CBD column is superior to the CBD process in terms of product quality, and conversion rate of acid. The CBD process is found to be a more attractive in terms of operating time saving, and annual profit improvement compared to the IBD, and MVD processes for the benzyl acetate system.The Higher Committee for Education Development in Iraq (HCED

Integration of design and control for large-scale applications: a back-off approach

Design and control are two distinct aspects of a process that are inherently related though these aspects are often treated independently. Performing a sequential design and control strategy may lead to poor control performance or overly conservative and thus expensive designs. Unsatisfactory designs stem from neglecting the connection of choices made at the process design stage that affects the process dynamics. Integration of design and control introduces the opportunity to establish a transparent link between steady-state economics and dynamic performance at the early stages of the process design that enables the identification of reliable and optimal designs while ensuring feasible operation of the process under internal and external disruptions. The dynamic nature of the current global market drives industries to push their manufacturing strategies to the limits to achieve a sustainable and optimal operation. Hence, the integration of design and control plays a crucial role in constructing a sustainable process since it increases the short and long-term profits of industrial processes. Simultaneous process design and control often results in challenging computationally intensive and complex problems, which can be formulated conceptually as dynamic optimization problems. The size and complexity of the conceptual integrated problem impose a limitation on the potential solution strategies that could be implemented on large-scale industrial systems. Thus far, the implementation of integration of design and methodologies on large-scale applications is still challenging and remains as an open question. The back-off approach is one of the proposed methodologies that relies on steady-state economics to initiate the search for optimal and dynamically feasible process design. The idea of the surrogate model is combined with the back-off approach in the current research as the key technique to propose a practical and systematic method for the integration of design and control for large-scale applications. The back-off approach featured with power series expansions (PSEs) is developed and extended to achieve multiple goals. The proposed back-off method focuses on searching for the optimal design and control parameters by solving a set of optimization problems using PSE functions. The idea is to search for the optimal direction in the optimization variables by solving a series of bounded PSE-based optimization problems. The approach is a sequential approximate optimization method in which the system is evaluated around the worst-case variability expected in process outputs. Hence, using PSE functions instead of the actual nonlinear dynamic process model at each iteration step reduces the computational effort. The method mostly traces the closest feasible and near-optimal solution to the initial steady-state condition considering the worst-case scenario. The term near-optimal refers to the potential deviations from the original locally optimum due to the approximation techniques considered in this work. A trust-region method has been developed in this research to tackle simultaneous design and control of large-scale processes under uncertainty. In the initial version of the back-off approach proposed in this research, the search space region in the PSE-based optimization problem was specified a priori. Selecting a constant search space for the PSE functions may undermine the convergence of the methodology since the predictions of the PSEs highly depend on the nominal conditions used to develop the corresponding PSE functions. Thus, an adaptive search space for individual PSE-optimization problems at every iteration step is proposed. The concept has been designed in a way that certifies the competence of the PSE functions at each iteration and adapts the search space of the optimization as the iteration proceeds in the algorithm. Metrics for estimating the residuals such as the mean of squared errors (MSE) are employed to quantify the accuracy of the PSE approximations. Search space regions identified by this method specify the boundaries of the decision variables for the PSE-based optimization problems. Finding a proper search region is a challenging task since the nonlinearity of the system at different nominal conditions may vary significantly. The procedure moves towards a descent direction and at the convergence point, it can be shown that it satisfies first-order KKT conditions. The proposed methodology has been tested on different case studies involving different features. Initially, an existent wastewater treatment plant is considered as a primary medium-scale case study in the early stages of the development of the methodology. The wastewater treatment plant is also used to investigate the potential benefits and capabilities of a stochastic version of the back-off methodology. Furthermore, the results of the proposed methodology are compared to the formal integration approach in a dynamic programming framework for the medium-scale case study. The Tennessee Eastman (TE) process is selected as a large-scale case study to explore the potentials of the proposed method. The results of the proposed trust-region methodology have been compared to previously reported results in the literature for this plant. The results indicate that the proposed methodology leads to more economically attractive and reliable designs while maintaining the dynamic operability of the system in the presence of disturbances and uncertainty. Therefore, the proposed methodology shows a significant accomplishment in locating dynamically feasible and near-optimal design and operating conditions thus making it attractive for the simultaneous design and control of large-scale and highly nonlinear plants under uncertainty