Optimization and technical-economic analysis of the production of 5-hydroxymethylfurfural from glucose / Otimização e análise técnico-econômica da produção de 5-hidroximetilfurfural a partir da glicose

In this study, the optimization of four processing schemes were developed for the production of 5 hydroxymethylfurfural (HMF), a chemical platform compound obtained from lignocellulosic biomass and used to make the connection with products from fossil resources. Despite its wide applicability, problems associated with its synthesis hindered its greater use. In this scenario the use of a computer program stands out, as the optimization of processes can be used, evaluating the factors and finding parameters that minimize the costs associated with its production. The optimization of these systems was made from the minimization of the total annual cost of the process carried out through the Aspen Plus® v.11 software, in addition, the economic feasibility of the process was analyzed and it was possible to obtain the HMF at 1.00$ .kg-1 and the co-product levulinic acid between 1.92 and 3.08 $.kg-1. It was also possible to note that among the operating costs, those from glucose had the greatest effect on its value, whereas in relation to equipment costs, reactors and distillation columns were the ones that had the greatest influence.

Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables the provision of equipment state monitoring services and the generation of decision-making for equipment operations. In this paper, we propose two machine learning models: (1) to forecast the amount of pentane (C5) content in the final product mixture; (2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models.

Algorithm and software for the optimal technological design of a system of simple distillation columns

Objectives. The formalized problem of the optimal design of distillation column systems belongs to the class of mixed integer nonlinear program problems. Discrete search variables are the number of trays in the rectifying and stripping sections of columns, whereas the continuous ones are the operating modes of columns. This study aimed to develop an algorithm and a software package for the optimal technological design of a system of simple distillation columns based on the criterion of total reduced capital and energy costs using rigorous mathematical distillation models.Methods. The solution to this problem is based on the branch and bound method. A computer model of the distillation column system was developed in the environment of the Aspen Hysys software package. The Inside–Out module was used as the distillation model. The developed algorithm is implemented in the software environment of the Matlab mathematical package. To solve the conditional optimization problem, a sequential quadratic programming method-based model was used. The interaction between software add-ins in Matlab and Aspen Hysys is implemented using a Component Object Model interface.Results. Approaches to obtain the lower and upper bounds of the optimality criterion and the branching method for the implementation of the branch and bound method have been developed. In addition, an algorithm for the optimal design of a distillation column of a given topology based on the branch and bound method has been developed. Furthermore, using Matlab, a software package that implements the developed algorithm and is integrated with the universal modeling software AspenHysys has been created.Conclusions. An algorithm and a software package have been developed and implemented that allows automating the design process of distillation column systems and integration with advanced mathematical programming packages, respectively. The performance of the algorithm and software package has been evaluated using the optimal design of the debutanization column as an example.

Cyber-Physical LPG Debutanizer Distillation Columns: Machine Learning-Based Soft Sensors for Product Quality Monitoring

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables to provide equipment state monitoring services and to generate decision-making for equipment operations. In this paper, we propose two machine learning models: 1) to forecast the amount of pentane (C5) content in the final product mixture; 2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach by using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models.

Development of a Dynamic Modeling Approach to Simulate a Segmented Distillation Column for Flexible Operation

The need for flexible process equipment has increased over the past decade in the chemical industry. However, process equipment such as distillation columns have limitations that significantly restrict flexible operation. We investigate a segmented tray column designed to allow flexible operation. The design consists of radial trays connected at the downcomer of each tray. Each segment can be operated separately, but depending on the capacity of the feed stream, additional segments can be activated or deactivated. The connection between the trays aims to transfer liquid from one stationary segment to the adjacent inactive segment, thereby reducing the time required for the start-up process. In a case study on the separation of methanol and water, we perform dynamic simulations to assess the reduction in the start-up time of inactive segments. The results confirm the advantages over standard tray designs. The segmented distillation column is a step towards improving the flexibility of separation operations.

Reconstruction of the hardware design for isopropylbenzene production for the production of ethylbenzene

The hardware design of the rectification unit for the production of isopropylbenzene in the presence of the AlCl3 catalyst is considered, the use of which has a number of problems associated with its corrosiveness, rapid deactivation, the complexity of regeneration, etc. One of the possible solutions to the listed technological disadvantages is the use of heterogeneous zeolite-containing catalysts. In this case, during the reconstruction of the existing production of isopropylbenzene in the technological scheme of separation of alkylate, three rectification columns are released, which are expediently used to separate the alkylate of ethylbenzene production. To study the possibility of using the discharging distillation columns, a computational experiment was carried out using the Honeywell UniSim Design modeling system, in which a model of the alkylate separation unit for ethylbenzene production was formed. The NRTL method was used as a mathematical package for calculating the thermodynamic properties of the mixture components. As a result of modeling the operation of the alkylate separation unit for each distillation column, the optimal operating process parameters were obtained: pressure of the top and bottom of the column, reflux ratio, temperature profile along the height of the column. The calculated material balance of the technological scheme shows that in the first distillation column, complete separation of benzene from the alkylation reaction mass is achieved, the second column ensures the production of commercial ethylbenzene, and in the third column, a fairly clear separation of diethylbenzene from the remaining components of the mixture is obtained. The performed computational experiment showed that for the organization of the stable operation of the technological scheme for the separation of alkylate in the production of ethylbenzene, it is possible to use all the considered distillation columns without changing their design parameters at a load on the reaction mass of alkylation of about 60 t / h.

Evaluation of mechanical vapor recompression in the reactive distillation of the N-butanol esterification process

The process of producing esters is usually performed through esterification in a reactor followed by a distillation column to separate the products. However, this design limits the reagent conversion. Reactive distillation is an alternative to get around this issue as it allows greater reagent conversions in reactions limited by chemical equilibrium. It is one of the most famous process intensification techniques. On the other hand, mechanical vapor recompression has been used to recycle waste heat to improve efficiency of conventional distillation columns. In this context, this work evaluated the inclusion of a mechanical vapor recompression system in a reactive distillation process to obtain n-butyl acetate via n-butanol esterification with acetic acid. Systems with and without recompression were simulated in an Aspen Plus™ environment. The addition of recompression resulted in a reduction of 33.65% in the annual cost of the process, while not significantly affecting the purity of the desired product and the reagents’ conversion. From an environmental point of view, the mechanical vapor recompression system adoption resulted in a 12.69% reduction in CO2 emissions, contributing positively to meeting the requirements of the environmental regulations.