Prediction of heat capacity of amine solutions using artificial neural network and thermodynamic models for CO2 capture processes

Afkhamipour, Morteza; Mofarahi, Masoud; Borhani, Tohid N.; Zanganeh, Masoud

doi:10.1007/s00231-017-2189-y

Cited by 12 publications

(5 citation statements)

References 22 publications

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“…Anyway, mathematical modeling is very complex and time consuming to estimate the mass properties according to the input effects. Therefore, artificial neural networks (ANNs) technique as a powerful tool provides a platform where solve these problems with logical precision and low computation times instead of mathematical modeling in drying process (Afkhamipour, Mofarahi, Borhani, & Zanganeh, ). Extensive investigations have been reported by means of ANNs for modeling and predicting the purposes in food science (Abbaspour‐Gilandeh, Jahanbakhshi, & Kaveh, ; Sun, Zhang, & Mujumdar, ).…”

Section: Introductionmentioning

confidence: 99%

“…Anyway, mathematical modeling is very complex and time consuming to estimate the mass properties according to the input effects. Therefore, artificial neural networks (ANNs) technique as a powerful tool provides a platform where solve these problems with logical precision and low computation times instead of mathematical modeling in drying process (Afkhamipour, Mofarahi, Borhani, & Zanganeh, 2018 (Amirsadri, Mousavirad, & Ebrahimpour-Komleh, 2018;Mirjalili, Mirjalili, & Lewis, 2014). GWO was first suggested by Mirjalili et al (2014) which is a potent model in solving of complex equations such as drying process.…”

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confidence: 99%

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Use of artificial intelligence for the estimation of effective moisture diffusivity, specific energy consumption, color and shrinkage in quince drying

Taghinezhad

Kaveh

Jahanbakhshi

et al. 2020

J Food Process Engineering

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In the study, drying process of quince fruit was accomplished in a microwave‐convective dryer (MCD). The experiments were carried out at microwave power levels of 100, 200, and 300 W, air temperatures of 40, 55, and 70°C, and air velocities of 0.5, 1, and 1.5 m/s. Nevertheless, three artificial intelligence techniques consisted of artificial neural networks (ANNs), particle swarm optimizer (PSO), and grey wolf optimizer (GWO) were evaluated to predict the parameters of Deff, SEC, ΔE, and Sb. In the evaluation the data by ANNs, input parameters of networks consisted the values of air temperature, microwave power, and air velocity. According to the results, the maximum values of effective moisture diffusivity ( Deff) and specific energy consumption (SEC) were 1.71 × 10−9 m2/s and 126.07 kWh/kg, respectively. In addition, minimum values of total change in color (ΔE) and shrinkage ( Sb) of quince achieved 10.85 and 33.85%, respectively. For predicting all parameters, three models used in the study represented good predictive capability with R2 > 0.97. The obtained results showed that the GWO model had better predictive performance than the ANN and PSO models. Practical Application Drying food and agricultural products by application of microwave‐hot air blend dryers can be a good alternative to hot air and microwave dryers. Microwave energy infiltrates the product and facilitates heat release from the product and thus reduces drying time compared to single dryers. The main aim of applying such different models, mathematical simulation or modeling in the drying technology of agricultural products is to transform physical qualities and their interactions into numerical quantities and mathematical relationships.

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Section: Introductionmentioning

confidence: 99%

“…Anyway, mathematical modeling is very complex and time consuming to estimate the mass properties according to the input effects. Therefore, artificial neural networks (ANNs) technique as a powerful tool provides a platform where solve these problems with logical precision and low computation times instead of mathematical modeling in drying process (Afkhamipour, Mofarahi, Borhani, & Zanganeh, 2018 (Amirsadri, Mousavirad, & Ebrahimpour-Komleh, 2018;Mirjalili, Mirjalili, & Lewis, 2014). GWO was first suggested by Mirjalili et al (2014) which is a potent model in solving of complex equations such as drying process.…”

mentioning

confidence: 99%

Use of artificial intelligence for the estimation of effective moisture diffusivity, specific energy consumption, color and shrinkage in quince drying

Taghinezhad

Kaveh

Jahanbakhshi

et al. 2020

J Food Process Engineering

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“…During the network training process, composite variables such as the number of hidden layers, neurons, and training epochs were assessed by trial and error. X j is weighted inputs for each neuron in jth layer and computed as below [44]:…”

Section: Model Development 231 Artificial Neural Networkmentioning

confidence: 99%

Application of Artificial Neural Networks, Support Vector, Adaptive Neuro-Fuzzy Inference Systems for the Moisture Ratio of Parboiled Hulls

Sharabiani

Kaveh²,

Taghinezhad

et al. 2022

Applied Sciences

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Drying as an effective method for preservation of crop products is affected by various conditions and to obtain optimum drying conditions it is needed to be evaluated using modeling techniques. In this study, an adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and support vector regression (SVR) was used for modeling the infrared-hot air (IR-HA) drying kinetics of parboiled hull. The ANFIS, ANN, and SVR were fed with 3 inputs of drying time (0–80 min), drying temperature (40, 50, and 60 °C), and two levels of IR power (0.32 and 0.49 W/cm2) for the prediction of moisture ratio (MR). After applying different models, several performance prediction indices, i.e., correlation coefficient (R2), mean square error index (MSE), and mean absolute error (MAE) were examined to select the best prediction and evaluation model. The results disclosed that higher inlet air temperature and IR power reduced the drying time. MSE values for the ANN, ANFIS tests, and SVR training were 0.0059, 0.0036, and 0.0004, respectively. These results indicate the high-performance capacity of machine learning methods and artificial intelligence to predict the MR in the drying process. According to the results obtained from the comparison of the three models, the SVR method showed better performance than the ANN and ANFIS methods due to its higher R2 and lower MSE.

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“…The authors also predicted the aqueous solution density and viscosity using the same method. Afkhamipour et al 74 selected concentration, temperature, molecular weight and CO 2 loading of the amine as the inputs (descriptors) to the ANN model to predict the heat capacity (C P ). Here, 3947 experimental data points representing heat capacity for 47 systems of amine-based solvents with a broad range of concentration and temperature were collected from published papers.…”

Section: Review Of the Ml-based Property Modelling Studiesmentioning

confidence: 99%

Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review

Yan

Borhani

Subraveti

et al. 2021

Energy Environ. Sci.

151

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Prediction of heat capacity of amine solutions using artificial neural network and thermodynamic models for CO2 capture processes

Cited by 12 publications

References 22 publications

Use of artificial intelligence for the estimation of effective moisture diffusivity, specific energy consumption, color and shrinkage in quince drying

Use of artificial intelligence for the estimation of effective moisture diffusivity, specific energy consumption, color and shrinkage in quince drying

Application of Artificial Neural Networks, Support Vector, Adaptive Neuro-Fuzzy Inference Systems for the Moisture Ratio of Parboiled Hulls

Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review

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