A rigorous simulation model of geothermal power plants for emission control

Vaccari, Marco; Pannocchia, Gabriele; Tognotti, Leonardo; Paci, Marco; Bonciani, Roberto

doi:10.1016/j.apenergy.2020.114563

Cited by 15 publications

(7 citation statements)

References 31 publications

(40 reference statements)

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“…Zhang et al [25] proposed a digital-twindriven smart manufacturing workshop carbon emission prediction and low-carbon control in order to achieve carbon emission reduction in intelligent manufacturing workshops. Vaccari et al [26] established a geothermal power generation simulation model to predict and control pollutant emissions, and the predicted value is essentially consistent with the actual measured value.…”

Section: Introductionmentioning

confidence: 62%

Data-Driven Low-Carbon Control Method of Machining Process—Taking Axle as an Example

2022

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It is an inevitable trend of enterprise development to optimize the low-carbon machining process and reduce the carbon emissions generated by this system. The traditional quality-based manufacturing method is no longer suitable for today’s concept of sustainable development. Therefore, a data-driven method based on uncertainty evaluation for low-carbon control in machining processes is proposed. Firstly, the framework for the data-driven method was established, then the data collection for the input and output in the machining process was carried out. Secondly, by establishing the carbon emission data model and analyzing data with carbon emission uncertainty evaluation indicators during processing, the carbon emission optimization strategy was proposed. Finally, axle processing technology was applied to the experimental verification, exploring the uncertainty of emissions finishing machining steps and other work sequences, while carrying out targeted strategy optimization, which verifies the feasibility and effectiveness of the method. The results show that the uncertainty of each process is reduced after optimization. This study provides theoretical and methodological support for promoting low-carbon emissions for manufacturing enterprises.

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Section: Introductionmentioning

confidence: 62%

Data-Driven Low-Carbon Control Method of Machining Process—Taking Axle as an Example

2022

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“…Circulating gas (stream 50) and fly-off gas (stream 34-2) were also key logistics in synthetic ammonia. The following variables were summarized as optimization variables, and actual factory data were used to reconcile the model: Some researchers have used UniSim's optimizer to design an optimization objective function [34] to make a model better conform to an actual situation. However, this study adopted a simpler optimization mode for data reconciliation [35].…”

Section: Data Reconciliationmentioning

confidence: 99%

Dynamic Simulation Analysis and Optimization of Green Ammonia Production Process under Transition State

Deng¹,

Huang

Li³

et al. 2022

Processes

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Ammonia is an important chemical raw material and the main hydrogen energy carrier. In the context of “carbon neutrality”, green ammonia produced using renewable energy is cleaner and produces less carbon than traditional ammonia production. Raw hydrogen dynamically fluctuates during green ammonia production because it is affected by the instability and intermittency of renewable energy; the green ammonia production process has frequent variable working conditions to take into account. Therefore, studying the transition state process of green ammonia is critical to the processing device’s stable operation. In this study, a natural gas ammonia production process was modified using green ammonia, and steady-state and dynamic models were established using UniSim. The model was calibrated using actual factory data to ensure the model’s reliability. Based on the steady-state model, hydrogen feed flow disturbance was added to the dynamic model to simulate the transition state process under variable working conditions. The change in system energy consumption in the transition state process was analyzed based on the data analysis method. The proportional-integral-derivative (PID) parameter optimization method was developed to optimize energy consumption under variable conditions of green ammonia’s production process. Based on this method, process control parameters were adjusted to shorten fluctuation time and reduce energy consumption.

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“…An Aspen HYSYS-based process simulator was used to produce various process operation data under different operating conditions of the HDT of crude oil with the CDU process. When sufficient plant operation data are available, then data reconciliation can be applied so that the Aspen HYSYS can match with real plant operation data [21]. Then, the data were utilized to construct neural network models.…”

Section: Modelling Of the Crude Oil Hdt Process With Cdu Using Bootst...mentioning

confidence: 99%

Multi-Objective Optimization of a Crude Oil Hydrotreating Process with a Crude Distillation Unit Based on Bootstrap Aggregated Neural Network Models

Muhsin

Zhang

2022

Processes

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This paper presents the multi-objective optimization of a crude oil hydrotreating (HDT) process with a crude atmospheric distillation unit using data-driven models based on bootstrap aggregated neural networks. Hydrotreating of the whole crude oil has economic benefit compared to the conventional hydrotreating of individual oil products. In order to overcome the difficulty in developing accurate mechanistic models and the computational burden of utilizing such models in optimization, bootstrap aggregated neural networks are utilized to develop reliable data-driven models for this process. Reliable optimal process operating conditions are derived by solving a multi-objective optimization problem incorporating minimization of the widths of model prediction confidence bounds as additional objectives. The multi-objective optimization problem is solved using the goal-attainment method. The proposed method is demonstrated on the HDT of crude oil with crude distillation unit simulated using Aspen HYSYS. Validation of the optimization results using Aspen HYSYS simulation demonstrates that the proposed technique is effective.

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A rigorous simulation model of geothermal power plants for emission control

Cited by 15 publications

References 31 publications

Data-Driven Low-Carbon Control Method of Machining Process—Taking Axle as an Example

Data-Driven Low-Carbon Control Method of Machining Process—Taking Axle as an Example

Dynamic Simulation Analysis and Optimization of Green Ammonia Production Process under Transition State

Multi-Objective Optimization of a Crude Oil Hydrotreating Process with a Crude Distillation Unit Based on Bootstrap Aggregated Neural Network Models

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