Comparative technoeconomic analysis and life cycle assessment of aromatics production from methanol and naphtha

Jiang, Jianrong; Feng, Xiao; Yang, Minbo; Wang, Yufei

doi:10.1016/j.jclepro.2020.123525

Cited by 37 publications

(29 citation statements)

References 30 publications

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“…(9) The specification of each product. (10) The purchase cost of each equipment. (11) The baremodule factor of each equipment.…”

Section: ■ Problem Statementmentioning

confidence: 99%

“…Zhang et al 9 addressed the techno-economic analysis for producing aromatics from methanol and pentane, and the results showed cofeeding of methanol and pentane with a mole ratio of 1:1 was more attractive. Jiang et al 10 presented a comparative techno-economic and environmental analysis of producing aromatics from methanol and naphtha for determining the competitive aromatics production route. Zhang et al 11 proposed two novel process designs by integrating methanol aromatization with light hydrocarbon aromatization to increase process profitability and environmental sustainability.…”

Section: ■ Introductionmentioning

confidence: 99%

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Simulation-Based Superstructure Optimization for the Synthesis Process of Aromatics Production from Methanol

Zhang

Jiang

Yang

et al. 2021

ACS Sustainable Chem. Eng.

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This work develops a novel superstructure considering a reaction−separation system for aromatics production from methanol, which consists of methanol aromatization, water removal, product separation, and light hydrocarbon aromatization units. On the basis of rigorous simulation of these units in Aspen HYSYS, a multiobjective simulation−optimization model is established for synthesis of methanol to aromatics process to simultaneously optimize the net present value and unit ReCiPe score. A matrix-based method is proposed to determine the Pareto-optimal curve and identify the optimal production routes through data interaction between MATLAB and Aspen HYSYS. The most profitable production route generates a net present value 39.0% higher than the lowest one, and the most environmentally sustainable production route shows a unit ReCiPe score 66.42% lower than the worst one. Monte Carlo simulation is used to investigate the effects of uncertain economic parameters on the three obtained Pareto-optimal production routes, which present robustness in the face of fluctuations of raw material and product prices as well as capital investment. Besides, the optimal production routes for variant reaction effluents are also analyzed to show the insight in production route selection.

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“…(9) The specification of each product. (10) The purchase cost of each equipment. (11) The baremodule factor of each equipment.…”

Section: ■ Problem Statementmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Simulation-Based Superstructure Optimization for the Synthesis Process of Aromatics Production from Methanol

Zhang

Jiang

Yang

et al. 2021

ACS Sustainable Chem. Eng.

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“…This study focuses on lignocellulosic biomass due to its high global abundance, and the technological reliability and data availability of the biomass conversion processes (Chen et al, 2019;Elkasabi et al, 2014;Hu et al, 2011;Zheng et al, 2017). The conversion routes investigated in this study are three thermochemical conversion methods, namely, pyrolysis (Ghorbannezhad et al, 2018;He et al, 2018;Zheng et al, 2014) including gas reactive pyrolysis (TGRP) and catalytic pyrolysis (CP), hydrothermal liquefaction (HTL) (Gerssen-Gondelach et al, 2014;Jensen et al, 2017), gasification-methanol-aromatics (GMA) (Jiang et al, 2020), and one biochemical conversion method, Diels-Alder reaction (Cheng and Huber, 2012;Wijaya et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Integral techno-economic comparison and greenhouse gas balances of different production routes of aromatics from biomass with CO2 capture

Fan

Meerman

Zhang

et al. 2022

Journal of Cleaner Production

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“…Based on the TEA result, a process can be evaluated based on specified parameters and assumptions for a multitude of purposes. It had been conducted conventionally over the past two decades for various purposes, ranging from the evaluation of economic factors [i.e., net present value (NPV) (Lubello et al, 2021), payback period (PBP) (Datas et al, 2019), internal rate of return (IRR) (Olszewski et al, 2017;Gönül et al, 2022), return of investment (ROI) (Qian et al, 2014), discounted cash flow rate of return (DFROR) (Phillips et al, 2011), capital cost (Han et al, 2016;Jiang et al, 2020), general costs (Medina-Martos et al, 2020), profit or revenue (Pérez et al, 2021;Wiatrowski et al, 2022), economic potential (Touili et al, 2018;Bagnato and Sanna, 2019), overall economic feasibility (Comidy et al, 2019)], process factors [i.e., energy saving percentage (Kong et al, 2020), process parameter optimization (Yang et al, 2018;Samani et al, 2022), efficiency of operation (Bock et al, 2021)], and environmental factor (Fahmy et al, 2021;Shawky Ismail et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Future era of techno-economic analysis: Insights from review

et al. 2022

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Techno-economic analysis (TEA) has been considered an important tool to evaluate the economic performance of industrial processes. Recently, the application of TEA has been observed to have exponential growth due to the increasing competition among businesses across various industries. Thus, this review presents a deliberate overview of TEA to inculcate the importance and relevance of TEA. To further support the aforementioned points, this review article starts with a bibliometric analysis to evaluate the applicability of TEA within the research community. Conventional TEA is widely known to be conducted via software modeling (i.e., Python, AMIS, MATLAB, Aspen HYSYS, Aspen Plus, HOMER Pro, FORTRAN, R, SysML and Microsoft Excel) without involving any correlation or optimization between the process and economic performance. Apart from that, due to the arrival of the industrial revolution (IR) 4.0, industrial processes are being revolutionized into smart industries. Thus, to retain the integrity of TEA, a similar evolution to smart industries is deemed necessary. Studies have begun to incorporate data-driven technologies (i.e., artificial intelligence (AI) and blockchain) into TEA to effectively optimize both processes and economic parameters simultaneously. With this, this review explores the integration of data-driven technologies in the TEA framework. From literature reviews, it was found that genetic algorithm (GA) is the most applied data-driven technology in TEA, while the applications of blockchain, machine learning (ML), and artificial neural network (ANN) in TEA are still considerably scarce. Not to mention other advanced technologies, such as cyber-physical systems (CPS), IoT, cloud computing, big data analytics, digital twin (DT), and metaverse are yet to be incorporated into the existing TEA. The inclusion of set-up costs for the aforementioned technologies is also crucial for accurate TEA representation of smart industries deployment. Overall, this review serves as a reference note for future process engineers and industry stakeholders who wish to perform relevant TEA, which is capable to cover the new state-of-art elements under the new modern era.

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Comparative technoeconomic analysis and life cycle assessment of aromatics production from methanol and naphtha

Cited by 37 publications

References 30 publications

Simulation-Based Superstructure Optimization for the Synthesis Process of Aromatics Production from Methanol

Simulation-Based Superstructure Optimization for the Synthesis Process of Aromatics Production from Methanol

Integral techno-economic comparison and greenhouse gas balances of different production routes of aromatics from biomass with CO2 capture

Future era of techno-economic analysis: Insights from review

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