Economic and environmental analysis of the cumene production process using computational simulation

Junqueira, Pedro G.; Mangili, Patrick V.; Santos, Rafael Oliveira dos; Santos, Lizandro de Sousa; Prata, Diego Martinez

doi:10.1016/j.cep.2018.06.010

Cited by 40 publications

(9 citation statements)

References 37 publications

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“…In Table 1, the kinetics of alkylation and transalkylation reactions are presented. [14] As shown, the activation energy of an undesirable reaction in the alkylation reactor is more significant than that of the cumene reaction. Therefore, running the reactions at low temperature increases the selectivity of cumene.…”

Section: Process Simulation 221 | Alkylation Process Simulationmentioning

confidence: 98%

Genetic‐based multi‐objective optimization of alkylation process by a hybrid model of statistical and artificial intelligence approaches

2021

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The purpose of this research is to find the optimal operating point in the production process of the cumene. Therefore, the production process was optimized through statistical and genetic algorithm-based methods. The performance of an alkylation reactor was optimized through maximizing the yield of cumene production. Response surface methodology (RSM) with design type of central composite was applied for design of experiment, modelling, and optimizing the process. The analysis of variance (ANOVA) was performed for finding the important operative parameters as well as their effects. The effects of three parameters including temperature, reactor length, and pressure on the alkylation process were investigated. Further, two types of feed-forward neural network were applied to model the alkylation reactor. To develop the neural network model, leave-one-out method was used. The best prediction performance belonged to a fitting network with 2 and 8 neurons in the hidden layer, respectively. This model was used for optimizing the performance of the alkylation reactor. The statistical and artificial intelligence systems were capable of prediction of cumene production yield in different conditions with R 2 of 0.9098 and 0.9986, respectively. Genetic algorithm-based optimization was performed by the developed neural network model. The maximum accessible value of cumene production yield was 0.7771, which can be achieved when the temperature, length of reactor, and column pressure are 160 C, 2 m, and 4000 kPa, respectively. By finding the optimal operating point in the cumene production process, capital cost, energy consumption, and other operating costs can be significantly reduced.

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Section: Process Simulation 221 | Alkylation Process Simulationmentioning

confidence: 98%

Genetic‐based multi‐objective optimization of alkylation process by a hybrid model of statistical and artificial intelligence approaches

2021

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“…Assuming full conversion of propylene as it was chosen as the limiting reactant, thus the reactor effluent consists of propane, benzene, p-diisopropyl benzene, and cumene. Hence, cumene is separated from the mixture of impurities by the method of distillation, based on the difference in boiling point [13]. There are 3 distillation columns considered in this design (from Figure 11), with the lightest component shall be separated in the first place [13].…”

Section: Separation and Purificationmentioning

confidence: 99%

Process Simulation & Sensitvity Analysis of Cumene Production from an Integrated Alkylation and Transalkylation Reaction

Hazmi¹

2022

JCPE

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Cumene is a very important petrochemical commodity, mainly to produce phenol and acetone. The overall growth rate for cumene capacity has been healthy, averaging slightly less than 3.5 % per year to reach 18 million metric tons per year in 2017. The purpose of this study is to generate a steady-state process simulation using ASPEN HYSYS version 10 to produce a small capacity of 10 ton/h of cumene with 99.99 wt % product purity. An alkylation reaction of benzene with propylene is carried out for producing cumene by using a zeolites catalyst as modeled by Badger technology. Transalkylation is also integrated into the system for eliminating unwanted products such as p-diisopropyl benzene. The proposed simulation flowsheet provides a good convergence overall result. The preliminary utility consumption obtained from the simulation consists of approximately 0.0418 kton/h of steam, 1.22 kton/h of cooling water, and 450 kW of electrical duty. Optimization is carried out in the simulation by conducting a sensitivity analysis study to find the optimum operating conditions of the alkylation reactor with a dimension of 1.3 m diameter and 4 m of length. The result shows that at an optimum value of B/P molar ratio of 7, reactant temperature of 170 oC, and reactant pressure of 3 MPa, the selectivity of cumene obtained is at a high value of 0.9446, while the percentage conversion of propylene to cumene obtained is at a high value of 99.99 %.

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“…For these motives, purification and separation equipment, such as distillation columns, must be utilized to obtain the main products at the desired commercial purity and to recycle unreacted feedstock, increasing the overall process' efficiency. Multiple conventional industrial plants employ these techniques: for example, the synthesis of methanol [3], butyl acetate [4], biodiesel [5], cumene [6], ethylbenzene [7] and maleic anhydride [8]. Purification process focused on reutilization, such as the purification of acetic acid [9] and toluene-benzene, are also found in conventional industry.…”

Section: Introductionmentioning

confidence: 99%

“…There are many strategies that can be performed to achieve this goal. For example, replacing the feedstock for ones with renewable sources, such as the use of biogas in energy generation and methanol synthesis [3,10], and the use of soy oil for biodiesel production [5], replacing a catalyst for another with greater efficiency in conversion and selectivity, as used in newer propylene polymerization plants [11], heat integration [4], single and multi-objective process optimization [5], and the development of process intensification techniques: reactive, double effect and dividing wall distillation [6,7,9,12], including membrane reactors [10].…”

Section: Introductionmentioning

confidence: 99%

Improvement of the monochlorobenzene separation process through heat integration: A sustainability-based assessment

PAIVA

Santos

Maia

et al. 2023

CI&CEQ

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Chlorobenzene is an important chemical intermediate in the production of commodities, such as herbicides, dyestuffs, and rubber. In this work, a heat integration was proposed for a monochlorobenzene separation process. The conventional process structure and the proposed integrated one were designed and simulated. An optimization focused on minimizing the cooling and heating costs was performed to obtain the best-operating conditions for the heat integration. The simulation of a utility plant, including cooling water and steam generation sections, was also carried out for more accurate estimations of CO2 emissions, water, energy consumption, and operating costs. The processes were evaluated and compared in terms of their sustainable performances using the eco-efficiency comparison index method and environmental and economic indicators, such as CO2 emission, water consumption, and utility cost, to assess the benefits of heat integration. The results demonstrated that the proposed strategy reduced around 57% of all environmental impacts and utility costs. As the composite evaluation index from the performance indicators showed, the proposed optimal heat integrated industrial plant significantly improved the initial processes? eco-efficiencies, up to 83%, proving a suitable strategy for a more sustainable process.

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Economic and environmental analysis of the cumene production process using computational simulation

Cited by 40 publications

References 37 publications

Genetic‐based multi‐objective optimization of alkylation process by a hybrid model of statistical and artificial intelligence approaches

Genetic‐based multi‐objective optimization of alkylation process by a hybrid model of statistical and artificial intelligence approaches

Process Simulation & Sensitvity Analysis of Cumene Production from an Integrated Alkylation and Transalkylation Reaction

Improvement of the monochlorobenzene separation process through heat integration: A sustainability-based assessment

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