Deep Learning Based Soft Sensor and Its Application on a Pyrolysis Reactor for Compositions Predictions of Gas Phase Components

Zhu, Wuming; Ma, Yan; Zhou, Yizhong; Benton, Michael G.; Romagnoli, José A.

doi:10.1016/b978-0-444-64241-7.50369-4

Cited by 41 publications

(19 citation statements)

References 6 publications

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“…CNNs have three principal components: convolution, pooling, and activation function. The convolution operation can extract features from the data set (through which their spatial information can be conserved), while the pooling operation reduces the feature maps dimensionality from the convolution operation [153]. It is worth noting that the convolution in this context defines the cross-correlation function, which is similar to convolution except that the kernel is not flipped [26]:…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

“…Moreover, these models mainly learn the global data features (neglecting the parameters' dynamics and local correlations) since they are usually fully linked networks [142]. On the other hand, a CNN can extract these dynamic and local features from the data samples by augmenting each sample's 2-D dynamic matrix, where several lagged input parameters are used to represent the process's dynamics [143,153].…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

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From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art

Paepae

Bokoro

Kyamakya

2021

Sensors

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Rapid urbanization, industrial development, and climate change have resulted in water pollution and in the quality deterioration of surface and groundwater at an alarming rate, deeming its quick, accurate, and inexpensive detection imperative. Despite the latest developments in sensor technologies, real-time determination of certain parameters is not easy or uneconomical. In such cases, the use of data-derived virtual sensors can be an effective alternative. In this paper, the feasibility of virtual sensing for water quality assessment is reviewed. The review focuses on the overview of key water quality parameters for a particular use case and the development of the corresponding cost estimates for their monitoring. The review further evaluates the current state-of-the-art in terms of the modeling approaches used, parameters studied, and whether the inputs were pre-processed by interrogating relevant literature published between 2001 and 2021. The review identified artificial neural networks, random forest, and multiple linear regression as dominant machine learning techniques used for developing inferential models. The survey also highlights the need for a comprehensive virtual sensing system in an internet of things environment. Thus, the review formulates the specification book for the advanced water quality assessment process (that involves a virtual sensing module) that can enable near real-time monitoring of water quality.

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Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art

Paepae

Bokoro

Kyamakya

2021

Sensors

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show abstract

“…According to the aforementioned points, we concluded that a model based on the convolutional neural networks was most suitable for developing the quality assessment module, as its combined feature extraction and classification into a single trainable model with a manageable number of tunable parameters [ 26 , 27 , 28 ]. Ensuring a substantial amount of data, the model was expected to be invulnerable regarding its classification capabilities due to the input’s data variations.…”

Section: Approachmentioning

confidence: 99%

Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase

Papacharalampopoulos

Tzimanis

Sabatakakis

et al. 2020

Sensors

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Vision technologies are used in both industrial and smart city applications in order to provide advanced value products due to embedded self-monitoring and assessment services. In addition, for the full utilization of the obtained data, deep learning is now suggested for use. To this end, the current work presents the implementation of image recognition techniques alongside the original the quality assessment of a Parabolic Trough Collector (PTC) reflector surface to locate and identify surface irregularities by classifying images as either acceptable or non-acceptable. The method consists of a three-step solution that promotes an affordable implementation in a relatively small time period. More specifically, a 3D Computer Aided Design (CAD) of the PTC was used for the pre-training of neural networks, while an aluminum reflector surface was used to verify algorithm performance. The results are promising, as this method proved applicable in cases where the actual part was manufactured in small batches or under the concept of customized manufacturing. Consequently, the algorithm is capable of being trained with a limited number of data.

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“…In other words, CNN paired with pooling operation can extract high-level features from the input data that includes the necessary information and has lower dimension. This means CNNs are easier to train and have fewer parameters compared to fully connected networks ( Goodfellow et al, 2016 ; Zhu et al, 2018 ; Feng et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Corn Yield Prediction With Ensemble CNN-DNN

2021

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We investigate the predictive performance of two novel CNN-DNN machine learning ensemble models in predicting county-level corn yields across the US Corn Belt (12 states). The developed data set is a combination of management, environment, and historical corn yields from 1980 to 2019. Two scenarios for ensemble creation are considered: homogenous and heterogenous ensembles. In homogenous ensembles, the base CNN-DNN models are all the same, but they are generated with a bagging procedure to ensure they exhibit a certain level of diversity. Heterogenous ensembles are created from different base CNN-DNN models which share the same architecture but have different hyperparameters. Three types of ensemble creation methods were used to create several ensembles for either of the scenarios: Basic Ensemble Method (BEM), Generalized Ensemble Method (GEM), and stacked generalized ensembles. Results indicated that both designed ensemble types (heterogenous and homogenous) outperform the ensembles created from five individual ML models (linear regression, LASSO, random forest, XGBoost, and LightGBM). Furthermore, by introducing improvements over the heterogenous ensembles, the homogenous ensembles provide the most accurate yield predictions across US Corn Belt states. This model could make 2019 yield predictions with a root mean square error of 866 kg/ha, equivalent to 8.5% relative root mean square and could successfully explain about 77% of the spatio-temporal variation in the corn grain yields. The significant predictive power of this model can be leveraged for designing a reliable tool for corn yield prediction which will in turn assist agronomic decision makers.

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Deep Learning Based Soft Sensor and Its Application on a Pyrolysis Reactor for Compositions Predictions of Gas Phase Components

Cited by 41 publications

References 6 publications

From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art

From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art

Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase

Corn Yield Prediction With Ensemble CNN-DNN

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