Prediction of H<sub>2</sub>S Solubility in Liquid Electrolytes by Multilayer Perceptron and Radial Basis Function Neural Networks

Barati-Harooni, Ali; Nasery, Saeid; Tatar, Afshin; Najafi‐Marghmaleki, Adel; Isafiade, Adeniyi J.; Bahadori, Alireza

doi:10.1002/ceat.201600110

Cited by 10 publications

(3 citation statements)

References 48 publications

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“…The neurons of each layer are connected by tunable parameters named biases and weights. The relationship among input and output parameters is determined using training of the network, which is a process for determining weights and biases to minimize the errors between model-predicted and real experimental values 28 . Different optimization methods such as Levenberg–Marquardt (LM) can be used in network training.…”

Section: The Theory Of ML Modelsmentioning

confidence: 99%

Estimation of the flow rate of pyrolysis gasoline, ethylene, and propylene in an industrial olefin plant using machine learning approaches

Abdi,

Mazloom,

Hadavimoghaddam

et al. 2023

Sci Rep

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Light olefins, as the backbone of the chemical and petrochemical industries, are produced mainly via steam cracking route. Prediction the of effects of operating variables on the product yield distribution through the mechanistic approaches is complex and requires long time. While increasing in the industrial automation and the availability of the high throughput data, the machine learning approaches have gained much attention due to the simplicity and less required computational efforts. In this study, the potential capability of four powerful machine learning models, i.e., Multilayer perceptron (MLP) neural network, adaptive boosting-support vector regression (AdaBoost-SVR), recurrent neural network (RNN), and deep belief network (DBN) was investigated to predict the product distribution of an olefin plant in industrial scale. In this regard, an extensive data set including 1184 actual data points were gathered during four successive years under various practical conditions. 24 varying independent parameters, including flow rates of different feedstock, numbers of active furnaces, and coil outlet temperatures, were chosen as the input variables of the models and the outputs were the flow rates of the main products, i.e., pyrolysis gasoline, ethylene, and propylene. The accuracy of the models was assessed by different statistical techniques. Based on the obtained results, the RNN model accurately predicted the main product flow rates with average absolute percent relative error (AAPRE) and determination coefficient (R2) values of 1.94% and 0.97, 1.29% and 0.99, 0.70% and 0.99 for pyrolysis gasoline, propylene, and ethylene, respectively. The influence of the various parameters on the products flow rate (estimated by the RNN model) was studied by the relevancy factor calculation. Accordingly, the number of furnaces in service and the flow rates of some feedstock had more positive impacts on the outputs. In addition, the effects of different operating conditions on the propylene/ethylene (P/E) ratio as a cracking severity factor were also discussed. This research proved that intelligent approaches, despite being simple and straightforward, can predict complex unit performance. Thus, they can be efficiently utilized to control and optimize different industrial-scale units.

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Section: The Theory Of ML Modelsmentioning

confidence: 99%

Estimation of the flow rate of pyrolysis gasoline, ethylene, and propylene in an industrial olefin plant using machine learning approaches

Abdi,

Mazloom,

Hadavimoghaddam

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…An MLP neural network with at least one hidden layer and enough number of hidden neurons can understand and estimate the behavior of more than 95 % of continuous phenomena [28]. The smallest portion of this smart simulator, namely, the neuron, has an interesting ability to extract the relationship between independent and dependent variable(s) [29,30]. The mathematical operations expressed by Eqs.…”

Section: Research Article 3 Neuro-based Approachmentioning

confidence: 99%

Design of a Neuro‐Based Computing Paradigm for Simulation of Industrial Olefin Plants

Esmaeili-Faraj

Vaferi

Bolhasani³

et al. 2021

Chem Eng & Technol

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A neuro-based computing technique is used for simulation of olefin plants at industrial scale. Artificial neural networks are applied to estimate the flow rate of the main products of the olefin unit from available information in terms of flow rate of feed streams and operating condition of furnaces. The structure of the smart model is determined through a trial-and-error procedure taking the real plant information over four successive years. The proposed paradigm estimates the tonnage of the product streams by an absolute average relative deviation in the range of 0.9 % for methane to 3.14 % for propylene. Results confirmed that this smart simulation not only presents accurate predictions, but is easy to use, straightforward, and can be simply employed for optimization and control of the unit.

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“…Ionic liquids (ILs) have recently earned much attentiveness as a suitable replacement for alkanolamine absorbents 8 . ILs are a novel family of solvents that have several prominent characteristics, such as non-inflammability, trivial vapor pressure, a vast liquid range, excellent temperature stability, good ion and electrical conductivity, electrochemical consistency, simplicity of recycling, high viscosity, and adjustable properties 9 – 12 .…”

Section: Introductionmentioning

confidence: 99%

Modeling of H2S solubility in ionic liquids: comparison of white-box machine learning, deep learning and ensemble learning approaches

Mousavi

Nakhaei-Kohani

Atashrouz

et al. 2023

Sci Rep

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In the context of gas processing and carbon sequestration, an adequate understanding of the solubility of acid gases in ionic liquids (ILs) under various thermodynamic circumstances is crucial. A poisonous, combustible, and acidic gas that can cause environmental damage is hydrogen sulfide (H2S). ILs are good choices for appropriate solvents in gas separation procedures. In this work, a variety of machine learning techniques, such as white-box machine learning, deep learning, and ensemble learning, were established to determine the solubility of H2S in ILs. The white-box models are group method of data handling (GMDH) and genetic programming (GP), the deep learning approach is deep belief network (DBN) and extreme gradient boosting (XGBoost) was selected as an ensemble approach. The models were established utilizing an extensive database with 1516 data points on the H2S solubility in 37 ILs throughout an extensive pressure and temperature range. Seven input variables, including temperature (T), pressure (P), two critical variables such as temperature (Tc) and pressure (Pc), acentric factor (ω), boiling temperature (Tb), and molecular weight (Mw), were used in these models; the output was the solubility of H2S. The findings show that the XGBoost model, with statistical parameters such as an average absolute percent relative error (AAPRE) of 1.14%, root mean square error (RMSE) of 0.002, standard deviation (SD) of 0.01, and a determination coefficient (R2) of 0.99, provides more precise calculations for H2S solubility in ILs. The sensitivity assessment demonstrated that temperature and pressure had the highest negative and highest positive affect on the H2S solubility in ILs, respectively. The Taylor diagram, cumulative frequency plot, cross-plot, and error bar all demonstrated the high effectiveness, accuracy, and reality of the XGBoost approach for predicting the H2S solubility in various ILs. The leverage analysis shows that the majority of the data points are experimentally reliable and just a small number of data points are found beyond the application domain of the XGBoost paradigm. Beyond these statistical results, some chemical structure effects were evaluated. First, it was shown that the lengthening of the cation alkyl chain enhances the H2S solubility in ILs. As another chemical structure effect, it was shown that higher fluorine content in anion leads to higher solubility in ILs. These phenomena were confirmed by experimental data and the model results. Connecting solubility data to the chemical structure of ILs, the results of this study can further assist to find appropriate ILs for specialized processes (based on the process conditions) as solvents for H2S.

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Prediction of H₂S Solubility in Liquid Electrolytes by Multilayer Perceptron and Radial Basis Function Neural Networks

Cited by 10 publications

References 48 publications

Estimation of the flow rate of pyrolysis gasoline, ethylene, and propylene in an industrial olefin plant using machine learning approaches

Estimation of the flow rate of pyrolysis gasoline, ethylene, and propylene in an industrial olefin plant using machine learning approaches

Design of a Neuro‐Based Computing Paradigm for Simulation of Industrial Olefin Plants

Modeling of H2S solubility in ionic liquids: comparison of white-box machine learning, deep learning and ensemble learning approaches

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Prediction of H2S Solubility in Liquid Electrolytes by Multilayer Perceptron and Radial Basis Function Neural Networks

Cited by 10 publications

References 48 publications

Estimation of the flow rate of pyrolysis gasoline, ethylene, and propylene in an industrial olefin plant using machine learning approaches

Estimation of the flow rate of pyrolysis gasoline, ethylene, and propylene in an industrial olefin plant using machine learning approaches

Design of a Neuro‐Based Computing Paradigm for Simulation of Industrial Olefin Plants

Modeling of H2S solubility in ionic liquids: comparison of white-box machine learning, deep learning and ensemble learning approaches

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Product

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Prediction of H₂S Solubility in Liquid Electrolytes by Multilayer Perceptron and Radial Basis Function Neural Networks