Economic comparison of a reactive distillation-based process with the conventional process for the production of ethyl tert-butyl ether (ETBE)

Domingues, Luís; Pinheiro, Carla I.C.; Oliveira, Nuno M.C.

doi:10.1016/j.compchemeng.2017.01.008

Cited by 16 publications

(11 citation statements)

References 30 publications

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“…Prices for steam and the components are listed in Tab. 1 and based on data found in the literature 39 (an equal price was assumed for IB and nBut). If

x_{i}^{(m)} > 0.99

for any component i, c o is calculated as:

true c_{o} = {\overset{\cdot}{m}}_{i} p_{i} 8000 \frac{normalh}{normala}

…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Automated Flowsheet Synthesis Using Hierarchical Reinforcement Learning: Proof of Concept

Göttl

Tönges

Grimm

et al. 2021

Chemie Ingenieur Technik

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Recently we showed that reinforcement learning can be used to automatically generate process flowsheets without heuristics or prior knowledge. For this purpose, SynGameZero, a novel two-player game has been developed. In this work we extend SynGameZero by structuring the agent's actions in several hierarchy levels, which improves the approach in terms of scalability and allows the consideration of more sophisticated flowsheet problems. We successfully demonstrate the usability of our novel framework for the fully automated synthesis of an ethyl tert-butyl ether process.

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“…Prices for steam and the components are listed in Tab. 1 and based on data found in the literature 39 (an equal price was assumed for IB and nBut). If

x_{i}^{(m)} > 0.99

for any component i, c o is calculated as:

true c_{o} = {\overset{\cdot}{m}}_{i} p_{i} 8000 \frac{normalh}{normala}

…”

Section: Methodsmentioning

confidence: 99%

“…The investment costs are estimated from the feed mass flowrate of the units. For reactors and distillation columns, base values for total investment costs are adapted from Domingues et al [39] for a mass flowrate of 9000 kg h -1 (the base values are listed in Tab. 1).…”

Section: Cost Functionmentioning

confidence: 99%

Automated Flowsheet Synthesis Using Hierarchical Reinforcement Learning: Proof of Concept

Göttl

Tönges

Grimm

et al. 2021

Chemie Ingenieur Technik

View full text Add to dashboard Cite

show abstract

“…The distillation columns were optimally designed by minimizing the TAC, using the response surface method [40]. The equations for the estimated cost were obtained from previous reports [41][42][43][44], and the Marshall and Swift equipment cost index of 1638.2 (2018) was used. The payout time of the investment cost was set to 5 years, and other cost estimation procedures have been described in a previous report [6].…”

Section: Unit Process Designmentioning

confidence: 99%

Energy‐Efficient Biobutanol Recovery Process Using 1‐Heptanol Extraction

Lee

Woo

et al. 2021

Chem Eng & Technol

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Biobutanol is an attractive fuel, owing to its abundant lignocellulose utilization and the similarity of its physical properties to those of gasoline. Biobutanol recovery from its fermentation broth is crucial for large-scale production because of the high costs involved. Extraction considerably reduces the energy demand for distillation, which is extensively used for its recovery. A new solvent, 1-heptanol, was used for extraction in this study, and its thermodynamic model parameters were derived based on liquid-liquid equilibrium data obtained via molecular simulations and experimental measurements. The results of the process design obtained with commercial design software indicated that the energy requirement was significantly lower than those of previously reported processes with comparable investments.

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“…The distillation columns were optimized in terms of their minimum total annualized cost (TAC), which is a combination of investment and operating costs. The cost equations used are reported in the literature [48,49], and a cost index of 1638.2 (the Marshall and Swift equipment cost index in 2018) was used. The payback time for the investment was 10 years, and the number of annual operation days was 300.…”

Section: Process Designmentioning

confidence: 99%

Energy-efficient bioethanol recovery process using deep eutectic solvent as entrainer

Lee

Woo

et al. 2021

Biomass Conv. Bioref.

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Bioethanol is commonly recovered from fermentation broth via distillation because it is the most economical and reliable process for large-scale industrial operations. Because extraction is a highly energy-efficient process applicable to low-composition bio-product separation, high-performance solvents are necessary for efficient bioethanol recovery. 2-Methyl pentanol, a branched long-chain alcohol, which was used as the extraction solvent, and a deep eutectic solvent, choline chloride and ethylene glycol (1:2), was employed as an entrainer for product refinement. Thermodynamic models of vapor–liquid equilibrium (VLE) and liquid–liquid equilibrium (LLE) systems were derived using molecular simulations and experimental results to develop the bioethanol extraction and refinement processes. The heat duty of the designed process was reduced by a quarter compared to that of previous recovery processes. Graphic abstract

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Economic comparison of a reactive distillation-based process with the conventional process for the production of ethyl tert-butyl ether (ETBE)

Cited by 16 publications

References 30 publications

Automated Flowsheet Synthesis Using Hierarchical Reinforcement Learning: Proof of Concept

Automated Flowsheet Synthesis Using Hierarchical Reinforcement Learning: Proof of Concept

Energy‐Efficient Biobutanol Recovery Process Using 1‐Heptanol Extraction

Energy-efficient bioethanol recovery process using deep eutectic solvent as entrainer

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