Experimental methods in chemical engineering: Process simulation

Tommaso, Jacopo De; Rossi, Francesco; Moradi, Nooshin; Pirola, Carlo; Patience, Gregory S.; Galli, F.

doi:10.1002/cjce.23857

Cited by 21 publications

(11 citation statements)

References 92 publications

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“…Simulation software can be typically used in the oil and gas industry to provide a quick process analysis and to facilitate engineering decisions. De Tommaso et al [1] highlighted the importance of process simulators to build digital twins, facilitating the implementation of industry 4.0 guidelines. Usually, simulation software are used to establish the project economics, through the optimization of each process step involved [2].…”

Section: Introductionmentioning

confidence: 99%

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Thermodynamics and Machine Learning Based Approaches for Vapor–Liquid–Liquid Phase Equilibria in n-Octane/Water, as a Naphtha–Water Surrogate in Water Blends

et al. 2021

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The prediction of phase equilibria for hydrocarbon/water blends in separators, is a subject of considerable importance for chemical processes. Despite its relevance, there are still pending questions. Among them, is the prediction of the correct number of phases. While a stability analysis using the Gibbs Free Energy of mixing and the NRTL model, provide a good understanding with calculation issues, when using HYSYS V9 and Aspen Plus V9 software, this shows that significant phase equilibrium uncertainties still exist. To clarify these matters, n-octane and water blends, are good surrogates of naphtha/water mixtures. Runs were developed in a CREC vapor–liquid (VL_ Cell operated with octane–water mixtures under dynamic conditions and used to establish the two-phase (liquid–vapor) and three phase (liquid–liquid–vapor) domains. Results obtained demonstrate that the two phase region (full solubility in the liquid phase) of n-octane in water at 100 °C is in the 10-4 mol fraction range, and it is larger than the 10-5 mol fraction predicted by Aspen Plus and the 10-7 mol fraction reported in the technical literature. Furthermore, and to provide an effective and accurate method for predicting the number of phases, a machine learning (ML) technique was implemented and successfully demonstrated, in the present study.

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Section: Introductionmentioning

confidence: 99%

“…The selection of the proper thermodynamic method to represent hydrocarbon/water mixtures is of major importance. To accomplish this, available decision trees were described by others [1,6]. For non-ideal mixtures, however, as is the case of n-octane/water systems, the NRTL model can be used.…”

Section: Introductionmentioning

confidence: 99%

Thermodynamics and Machine Learning Based Approaches for Vapor–Liquid–Liquid Phase Equilibria in n-Octane/Water, as a Naphtha–Water Surrogate in Water Blends

et al. 2021

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“…Digital twin solutions have already been applied for ergonomic optimization [131], optimizing the behavior of the system during the design phase [132] and multirobot manufacturing cell optimization [47]. Process simulations are also possible [133]. e approach seems to have become a key enabler of further Industry 4.0 solutions and their optimizations in the future, e.g., autonomous maintenance [60].…”

Section: Digital Twinmentioning

confidence: 99%

Industry 4.0‐Driven Development of Optimization Algorithms: A Systematic Overview

et al. 2021

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The Fourth Industrial Revolution means the digital transformation of production systems. Cyber-physical systems allow for the horizontal and vertical integration of these production systems as well as the exploitation of the benefits via optimization tools. This article reviews the impact of Industry 4.0 solutions concerning optimization tasks and optimization algorithms, in addition to the identification of the new R&D directions driven by new application options. The basic organizing principle of this overview of the literature is to explore the requirements of optimization tasks, which are needed to perform horizontal and vertical integration. This systematic review presents content from 900 articles on Industry 4.0 and optimization as well as 388 articles on Industry 4.0 and scheduling. It is our hope that this work can serve as a starting point for researchers and developers in the field.

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“…Here, we simulate the MRU process with ASPEN Plus and developed a Python code to conduct an economic analysis [37].We study the techno-economic analysis of the application of the MRU for treating biogas, natural gas, and landfill gas.…”

Section: Figurementioning

confidence: 99%

Gas to Liquids Techno-Economics of Associated Natural Gas, Bio Gas, and Landfill Gas

et al. 2021

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Methane is the second highest contributor to the greenhouse effect. Its global warming potential is 37 times that of CO2. Flaring-associated natural gas from remote oil reservoirs is currently the only economical alternative. Gas-to-liquid (GtL) technologies first convert natural gas into syngas, then it into liquids such as methanol, Fischer–Tropsch fuels or dimethyl ether. However, studies on the influence of feedstock composition are sparse, which also poses technical design challenges. Here, we examine the techno-economic analysis of a micro-refinery unit (MRU) that partially oxidizes methane-rich feedstocks and polymerizes the syngas formed via Fischer–Tropsch reaction. We consider three methane-containing waste gases: natural gas, biogas, and landfill gas. The FT fuel selling price is critical for the economy of the unit. A Monte Carlo simulation assesses the influence of the composition on the final product quantity as well as on the capital and operative expenses. The Aspen Plus simulation and Python calculate the net present value and payback time of the MRU for different price scenarios. The CO2 content in biogas and landfill gas limit the CO/H2 ratio to 1.3 and 0.9, respectively, which increases the olefins content of the final product. Compressors are the main source of capital cost while the labor cost represents 20–25% of the variable cost. An analysis of the impact of the plant dimension demonstrated that the higher number represents a favorable business model for this unit. A minimal production of 7,300,000 kg y−1 is required for MRU to have a positive net present value after 10 years when natural gas is the feedstock.

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Experimental methods in chemical engineering: Process simulation

Cited by 21 publications

References 92 publications

Thermodynamics and Machine Learning Based Approaches for Vapor–Liquid–Liquid Phase Equilibria in n-Octane/Water, as a Naphtha–Water Surrogate in Water Blends

Thermodynamics and Machine Learning Based Approaches for Vapor–Liquid–Liquid Phase Equilibria in n-Octane/Water, as a Naphtha–Water Surrogate in Water Blends

Industry 4.0‐Driven Development of Optimization Algorithms: A Systematic Overview

Gas to Liquids Techno-Economics of Associated Natural Gas, Bio Gas, and Landfill Gas

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