A process simulation based benchmarking approach for evaluating energy consumption of a chemical process system

Yang, Feifei; Liu, Yongzhong; Liu, Guilian

doi:10.1016/j.jclepro.2015.10.025

Cited by 17 publications

(6 citation statements)

References 27 publications

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“…It is a metric often used in pharmaceutical chemistry as it can determine the amount of waste relative to the quantity of product being generated, showing the efficiency of the process. , PMI = ∑ input mass ( reactant , chemical , catalyst , and solvent ) ∑ product mass Another alternative green metric is the energy consumption of the reaction (eq ); this can aid in determination of the greenness of a reaction. On the basis of this metric, reactions can be designed with an estimate of the energy consumption, and so strategies can be proposed for improving the energy efficiency to enhance the sustainability of the process energy consumption = power × time 1000 An introduction and accompanying equations, as seen above in eqs –, are supplied for students and instructors on page 3 of the student laboratory manuscript in the Supporting Information.…”

Section: Introductionmentioning

confidence: 99%

Byproduct Valorization: From Spent Coffee Grounds to Fatty Acid Ethyl Esters

et al. 2022

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A laboratory experiment was designed to provide students with an introduction to byproduct valorization by producing an analogue of biodiesel (fatty acid ethyl esters, FAEE) via lipid extraction and subsequent transesterification from spent coffee grounds (SCG). Valorization is the process of upgrading underutilized or discarded wastes or byproducts into chemicals, materials, and (bio)energy. Upon isolation of the intermediate and FAEE, students analyzed the respective spectroscopic characteristics using infrared and nuclear magnetic resonance spectroscopy as well as evaluated gas chromatography spectra that were provided. Associated green metrics for the reaction were determined to obtain a quantitative measure of the “greenness” of the experiment. In this laboratory experiment, students are introduced to key concepts such as whole systems thinking and the United Nations Sustainable Development Goals where they apply these frameworks to real-world problems such as consumption and waste disposal. In addition to providing a greener alternative to traditional methods for biodiesel analogue production from SCG by solvent alterations, this experiment demonstrates the value of waste that would otherwise be overlooked to generate products that can help reduce the continued use of finite fossil fuels.

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Section: Introductionmentioning

confidence: 99%

Byproduct Valorization: From Spent Coffee Grounds to Fatty Acid Ethyl Esters

et al. 2022

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“…The current energy consumption benchmarking methods mainly concentrate on collecting the operational data of the considered system and performing longitudinal and transverse data statistical analysis. 15 However, the process structures are diverse for energy-intensive chemical production processes such as natural gas purification; and the incoming parameters or operating parameters may frequently fluctuate. As a result, it is not easy to obtain the optimal performance through data reconciliation and acquisition.…”

Section: Introductionmentioning

confidence: 99%

“…17 Some commercial software is available and used widely in the petrochemical industry for process optimization and the determination of optimal performance (eg, gPROMS, AspenPlus, etc). In this regard, Yang et al 15 implemented the energy consumption benchmarking for a natural gas purification system from the perspective of system engineering. They determined the energy consumption indicators and relevant definitions of the basic system to evaluate the system to be analyzed and obtain the most advanced energy performance through continuous updating of the basic system that is simulated by ASPEN Plus.…”

Section: Introductionmentioning

confidence: 99%

An energy‐efficiency evaluation method for high‐sulfur natural gas purification system using artificial neural networks and particle swarm optimization

Qiu

2021

Intl J of Energy Research

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Natural gas purification, especially with high sulfur content, is an energyintensive chemical production process, for which there are considerable differences in the energy consumption patterns of different purification plants. Therefore, these purification plants are required to establish a universal evaluation standard for energy consumption performance. This article proposed a novel approach to evaluate the energy efficiency of the natural gas purification process from the systems engineering perspective. An evaluation system is established for the hierarchical indicators of energy consumption using this technique providing the detailed definition of evaluation indicators for process, unit, and device. At the same time, a technical route is proposed for intelligent algorithm optimization and artificial neural network modeling based on historical operation data of the plant to discover the energy consumption benchmarks under various raw gas flow rates. Using this proposed method, the energy consumption efficiency can be evaluated while analyzing the energysavings potential of these natural gas purification plants with various process types or raw gas characteristics. Furthermore, the model based on historical operating data can objectively and truly reflect the plant's energy consumption features; therefore, the plant's energy consumption can be decreased to benchmark by adjusting the corresponding operation parameters. Ultimately, the computational process of the energy consumption benchmark is described thoroughly for a high-sulfur natural gas purification plant.

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“…Despite the increasing use of low carbon energy sources, fossil fuels remain as the dominant energy sources worldwide [1,2], with their share accounting for 81% in 2008 to 74% in 2035 [3,4]. The rising demand for fossil fuels led to CO 2 emissions rising from 29.3 gigatons (Gt) in 2008 to 35.4 Gt in 2035 [5,6].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Multi-Objective Optimization of Machining Allowance Distribution and Parameters for Energy Saving Strategy

Hong

Tang

et al. 2020

Sustainability

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Machining allowance distribution and related parameter optimization of machining processes have been well-discussed. However, for energy saving purposes, the optimization priorities of different machining phases should be different. There are often significant incoherencies between the existing research and real applications. This paper presents an improved method to optimize machining allowance distribution and parameters comprehensively, considering energy-saving strategy and other multi-objectives of different phases. The empirical parametric models of different machining phases were established, with the allowance distribution problem properly addressed. Based on previous analysis work of algorithm performance, non-dominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition were chosen to obtain Pareto solutions. Algorithm performances were compared based on the efficiency of finding the Pareto fronts. Two case studies of a cylindrical turning and a face milling were carried out. Results demonstrate that the proposed method is effective in trading-off and finding precise application scopes of machining allowances and parameters used in real production. Cutting tool life and surface roughness can be greatly improved for turning. Energy consumption of rough milling can be greatly reduced to around 20% of traditional methods. The optimum algorithm of each case is also recognized. The proposed method can be easily extended to other machining scenarios and can be used as guidance of process planning for meeting various engineering demands.

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A process simulation based benchmarking approach for evaluating energy consumption of a chemical process system

Cited by 17 publications

References 27 publications

Byproduct Valorization: From Spent Coffee Grounds to Fatty Acid Ethyl Esters

Byproduct Valorization: From Spent Coffee Grounds to Fatty Acid Ethyl Esters

An energy‐efficiency evaluation method for high‐sulfur natural gas purification system using artificial neural networks and particle swarm optimization

Analysis of Multi-Objective Optimization of Machining Allowance Distribution and Parameters for Energy Saving Strategy

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