Prediction of chemical exergy of syngas from downdraft gasifier by means of machine learning

Sezer, Senem; Kartal, Furkan; Özveren, Uğur

doi:10.1016/j.tsep.2021.101031

Cited by 10 publications

(5 citation statements)

References 71 publications

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“…Another advantage of Aspen Plus ® is that solid components such as biomass, coal can be correctly handled in the well-designed model for gasification applications [31,32]. The steady-state and equilibrium-based model of the downdraft gasifier for microalgae gasification has been developed using Aspen Plus ® V11 [33]. The flow chart of the downdraft gasifier in the Aspen Plus ® software was presented in Figure 1.…”

Section: Downdraft Gasifier Modelmentioning

confidence: 99%

Investigation of Microalgae Gasification Under Steam Atmosphere in Downdraft Gasifier by Using Aspen Plus®

KEKİK

Özveren

2022

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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Energy production is facing the environmental and economic issues due to growing population and uncertainties about the fossil fuels. These concerns prompt the researchers to find widely available and renewable alternative such as biomass instead of the fossil fuels. Microalgae is one of the promising biofuels owing to its rapid growth speed and higher calorific value. Steam gasification is an alternative way converting biomass to syngas with higher H2 and lower CO2 content compared with the other thermochemical conversion processes. In the present work, the downdraft gasifier model was developed by using Aspen Plus® program that has the capability to investigate the performance of microalgae gasification. Before the performance evaluation of the gasification, validation of the model successfully completed and exit gas compositions of H2, CO2, CO and CH4 were found very close for experimental study and the developed model. Effects of the main parameters of the process such as steam/biomass ratio and gasification temperature were assessed on the syngas composition and higher heating value (HHV) of syngas. The obtained results were stated that the increment in the temperature showed great effect on the H2 and CO compositions of syngas, they increased from 50.72% to 56.47% and 28.11% to 28.84% respectively. The simulation results also illustrated that the rising of the S/B ratio was favored the steam related reactions and increased the H2 content in syngas. However decreasing trend of the CH4 caused reducing of the HHV of syngas as a function of temperature and steam as well.

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Section: Downdraft Gasifier Modelmentioning

confidence: 99%

Investigation of Microalgae Gasification Under Steam Atmosphere in Downdraft Gasifier by Using Aspen Plus®

KEKİK

Özveren

2022

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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“…This has motivated research groups to explore data-driven approaches. [33][34][35][36] Some works use gasication data obtained from thermodynamic simulation studies, 37,38 making easier to create datasets compared to experiments, which can provide a wider overview of the process performance, but the results can vary considerably from real gasiers. Other studies collect data from several works in the literature and usually focus on the effect of the process operating parameters on the gasication outputs.…”

Section: Introductionmentioning

confidence: 99%

Biomass to energy: a machine learning model for optimum gasification pathways

et al. 2023

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“…From the literature, ANN is helpful in a complex thermochemical process to evaluate the production rate, and the exergy transaction of the process can be predicted with low relative error. 33 Prediction of exergy was made with ANN and ANFIS for better process parameters and operating conditions of the onion drying process. 34 Hydrogen production is predicted with ML models, likely GPR, ANN, SVM, and RF, to evaluate the process performance.…”

Section: Introductionmentioning

confidence: 99%

“…37 ANN model achieved RMSE values to determine the chemical exergy of the syngas process data extracted from the Aspen Plus. 33 ML-based ANN algorithms used in the oxygencarrying process, the high value of R 2 (0.94) and low MAE (0.057) is achieved with an optimal ANN model. 38 This paper's novelty suggested a sustainable operating procedure for the chloromethane industry based on thermodynamic analysis of the MC plant.…”

Section: Introductionmentioning

confidence: 99%

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Energetic, exergetic analysis and machine learning of methane chlorination process for methyl chloride production

Gollangi

Rao

2022

Energy & Environment

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Nowadays, with the growing demand for energy and effective utilization of various available sources with the exorable techniques and approaches to maximize the efficiency of energy systems. This work has developed the synthesis of Methyl chloride (MC) from the methane chlorination process using the ASPEN HYSYS simulation tool. A Searchable analysis has been done on thermodynamic derivatives (likely Energy, Exergy) to probation on the entire process. This analysis calculates all process components’ energy loss, destruction and energy, and exergy efficiencies. A heavier energy loss has been found at Reactor (ERV) with 1785.5 kW and exergy destruction of 18.8% share. Heat Exchanger Network (HEN) has energy loss (960.32kW) & exergy destruction (791.29kW). The proposed new retrofit sustainable model recovered the waste heat from the HEN and achieved energy efficiency of 87.6% and exergy efficiency of 87.3% of the total MC process. Four Machine learning models were developed for the reactor (ERV) process to predict exergy destruction. The artificial Neural network (ANN) gave good testing predictions, followed by the Random Forest (RF) with a determination coefficient (R2) of 0.999957 and 0.999981.

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Prediction of chemical exergy of syngas from downdraft gasifier by means of machine learning

Cited by 10 publications

References 71 publications

Investigation of Microalgae Gasification Under Steam Atmosphere in Downdraft Gasifier by Using Aspen Plus®

Investigation of Microalgae Gasification Under Steam Atmosphere in Downdraft Gasifier by Using Aspen Plus®

Biomass to energy: a machine learning model for optimum gasification pathways

Energetic, exergetic analysis and machine learning of methane chlorination process for methyl chloride production

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