Deep learning based dynamic behavior modelling and prediction of particulate matter in air

Inapakurthi, Ravi kiran; Miriyala, Srinivas Soumitri; Mitra, Kishalay

doi:10.1016/j.cej.2021.131221

Cited by 43 publications

(20 citation statements)

References 34 publications

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“…A similar optimization framework is also used by Inapakurthi and Mitra to determine the hyper-parameters of the support vector regression model for an industrial grinding process. In ref , the optimal neural architecture for the modeling and prediction of particulate matter in air is also determined via the multi-objective optimization method. Table lists the hyper-parameter settings used in this paper.…”

Section: Methodsmentioning

confidence: 99%

Data-Based Modeling of a Nonexplicit Two-Time Scale Process via Multiple Time-Scale Recurrent Neural Networks

Jian

Zabiri

Ramasamy

2022

Ind. Eng. Chem. Res.

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Modern chemical process plants are typically very complex in nature due to the various material and energy recycling streams, including the implementation of process intensification in order to improve the sustainability factor. Looking at each individual unit operation, there are inherent nonlinear properties that may often involve physical and chemical phenomena occurring on different time scales. In addition, there exist chemical processes that also inherently have dynamics that are multi time-scale in nature. However, designing the control system for those integrated and intensified processes poses significant difficulties because they naturally lead to the dynamics of the network having multiple timescale behaviors and a reduced number of degrees of freedom. The status quo approach to handle multiple time scales would be to separate the plant-wide dynamics into their fast and slow components and implement a hierarchical control structure, but the current practices would lead to computational issues when being applied to model-based controllers due to the inversion of ill-conditioned and stiff differential algebraic equation models under large time-scale separation. To address this issue, this study proposes an alternative approach to modeling multi time-scale processes based on the use of multiple time-scale recurrent neural networks (MTRNNs). The analysis is demonstrated via a benchmark multi time-scale continuous stirred tank reactor (CSTR) case study, using input−output data generated via simulation of nonlinear dynamical equations of the CSTR with large parameters (reciprocal of the singular perturbation small parameter) in the form of 1/ε as the large heat transfer coefficient. The MTRNN model is constructed with groups of neuronal nodes grouped together based on their assigned time constant (also known as decay rate), which leads to an improved prediction performance when compared to other common modeling methods. The prediction performance of the proposed MTRNN model when applied to the multi time-scale CSTR results in an R 2 value of 0.9997, in addition to an average of 75 times lower root mean square error when compared to that of the nonlinear autoregressive network with exogenous inputs (NARX) and transfer function methods, implying its higher potential efficacy in modeling complex multi timescale processes.

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Section: Methodsmentioning

confidence: 99%

Data-Based Modeling of a Nonexplicit Two-Time Scale Process via Multiple Time-Scale Recurrent Neural Networks

Jian

Zabiri

Ramasamy

2022

Ind. Eng. Chem. Res.

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“…In addition, an operational model was constructed for this process using a hybrid approach of a deep neural network and a first-principles model . ANNs were further used to determine optimum operating conditions for chemical and industrial processes, − which contributed to maximizing the feasibility of novel processes from economic and safety perspectives.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Operational Modeling of an Electrochemical Reactor: Handling Data Variability and Improving Empirical Models

Liu

Canuso

Jang

et al. 2022

Ind. Eng. Chem. Res.

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Electrochemical reduction of carbon dioxide (CO2) has received increasing attention with the recent rise in awareness of climate change and the increase in electricity supply from clean energy sources. However, because of the complexity of its reaction mechanism and the largely unknown electron transfer pathways, the development of a first-principles-based operational model of a CO2 electrocatalytic reactor is still in its infancy. This work proposes a methodology to develop a feed-forward neural network (FNN) model to capture the input–output relationship of an experimental electrochemical reactor from experimental data that are obtained from easy-to-implement sensors. This FNN model is computationally efficient and can be used in real-time to determine energy-optimal reactor operating conditions. To further account for the uncertainty of the experimental data, the maximum likelihood estimation (MLE) method is adopted to construct a statistical neural network, which is demonstrated to be able to address a usual overfitting problem that occurs in the standard FNN model. In addition, by comparing the neural network with an empirical first-principles-based model, it is demonstrated that the neural network model achieves improved prediction accuracy with respect to experimentally determined input–output operating conditions. Finally, the insights obtained from the FNN model and the limitations identified of the empirical, first-principles model (EFP model) are used to propose specific modifications to the EFP model to improve its prediction capability.

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“…It also provides accurate, reliable, and robust predictions for the wind power of seven wind farms in Europe. Inapakurthi [19] used RNN and LSTM to capture the dynamic trends of 15 environmental parameters, including particulate matter and pollutants in the atmosphere that cause long-term health hazards. In addition to these models, the attention models have also received attention from researchers.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Parameter Prediction Method for Coal Slime Blending Circulating Fluidized Bed Units

2022

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Coal slime blending can effectively improve the utilization rate of fossil fuels and reduce environmental pollution. However, the combustion in the furnace is unstable due to the empty pump phenomenon during the coal slurry transport. The combustion instability affects the material distribution in the furnace and harms the unit operation. The bed pressure in the circulating fluidized bed unit reflects the amount of material in the furnace. An accurate bed pressure prediction model can reflect the future material quantity in the furnace, which helps adjust the operation of the unit in a timely fashion. Thus, a deep learning-based prediction method for bed pressure is proposed in this paper. The Pearson correlation coefficient with time correction was used to screen the input variables. The Gaussian convolution kernels were used to implement the extraction of inertial delay characteristics of the data. Based on the computational theory of the temporal attention layer, the model was trained using the segmented approach. Ablation experiments verified the innovations of the proposed method. Compared with other models, the mean absolute error of the proposed model reached 0.0443 kPa, 0.0931 kPa, and 0.0345 kPa for the three data sets, respectively, which are better than those of the other models.

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Deep learning based dynamic behavior modelling and prediction of particulate matter in air

Cited by 43 publications

References 34 publications

Data-Based Modeling of a Nonexplicit Two-Time Scale Process via Multiple Time-Scale Recurrent Neural Networks

Data-Based Modeling of a Nonexplicit Two-Time Scale Process via Multiple Time-Scale Recurrent Neural Networks

Machine Learning-Based Operational Modeling of an Electrochemical Reactor: Handling Data Variability and Improving Empirical Models

A Deep Learning-Based Parameter Prediction Method for Coal Slime Blending Circulating Fluidized Bed Units

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