Robust probabilistic principal component regression with switching mixture Gaussian noise for soft sensing

Sadeghian, Anahita; Jan, Nabil Magbool; Wu, Ouyang; Huang, Biao

doi:10.1016/j.chemolab.2022.104491

Cited by 10 publications

(3 citation statements)

References 20 publications

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“…Virtual sensors are actually predictive mathematical models which use explanatory variables (EVs, i.e., easily measurable variables like pressure and flow rate) as inputs and estimates of the KVs as outputs, having the benefits of no measurement delays and low costs [7,8]. Therefore, the virtual sensor effectively compensates for the shortcomings of the offline laboratory analysis and hardware sensor [9,10]. As a result, virtual sensors have been intensively studied and widely used in the hydrogen production process.…”

Section: Introductionmentioning

confidence: 99%

Virtual Sensing of Key Variables in the Hydrogen Production Process: A Comparative Study of Data-Driven Models

Yao,

Xing,

Zuo

et al. 2024

Sensors

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Hydrogen is an ideal energy carrier manufactured mainly by the natural gas steam reforming hydrogen production process. The concentrations of CH4, CO, CO2, and H2 in this process are key variables related to product quality, which thus need to be controlled accurately in real-time. However, conventional measurement methods for these concentrations suffer from significant delays or huge acquisition and upkeep costs. Virtual sensors effectively compensate for these shortcomings. Unfortunately, previously developed virtual sensors have not fully considered the complex characteristics of the hydrogen production process. Therefore, a virtual sensor model, called “moving window-based dynamic variational Bayesian principal component analysis (MW-DVBPCA)” is developed for key gas concentration estimation. The MW-DVBPCA considers complicated characteristics of the hydrogen production process, involving dynamics, time variations, and transportation delays. Specifically, the dynamics are modeled by the finite impulse response paradigm, the transportation delays are automatically determined using the differential evolution algorithm, and the time variations are captured by the moving window method. Moreover, a comparative study of data-driven virtual sensors is carried out, which is sporadically discussed in the literature. Meanwhile, the performance of the developed MW-DVBPCA is verified by the real-life natural gas steam reforming hydrogen production process.

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Section: Introductionmentioning

confidence: 99%

Virtual Sensing of Key Variables in the Hydrogen Production Process: A Comparative Study of Data-Driven Models

Yao,

Xing,

Zuo

et al. 2024

Sensors

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“…8,9 Therefore, the soft analyzer is delay-free, easy and cheap to maintain, and it has now been developed as a promising solution to the issues associated with laboratory analysis and hardware analyzer. 10 Up to now, the soft analyzer has been researched in-depth and applied extensively in the hydrocracking process.…”

Section: Introductionmentioning

confidence: 99%

“…The soft analyzer, which is also known as “soft sensor” or “virtual sensor”, virtually estimates the sulfur content of tail oil by developing a mathematical predictive model integrating process knowledge and operation data and by taking easy‐to‐measure variables (also called secondary variables, such as flow rate, pressure, and temperature) as inputs 8,9 . Therefore, the soft analyzer is delay‐free, easy and cheap to maintain, and it has now been developed as a promising solution to the issues associated with laboratory analysis and hardware analyzer 10 . Up to now, the soft analyzer has been researched in‐depth and applied extensively in the hydrocracking process.…”

Section: Introductionmentioning

confidence: 99%

Soft analyzer for sulfur content in tail oil of hydrocracking process based on adaptively regularized dynamic polynomial PLS

Yao,

Shen,

Qiao

et al. 2023

Asia-Pacific J Chem Eng

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Hydrocracking process is an essential and widely used means to remove impurities such as sulfur, nitrogen, and oxygen from heavy oil in refineries. The sulfur content in the tail oil of the hydrogenation process is a key quality index that must be strictly monitored and controlled. However, the traditional measurement ways of the sulfur content suffer from either large delays or high investment and maintenance costs. To this end, a predictive model named “adaptively regularized dynamic polynomial partial least squares (ARDPPLS)” is proposed to develop soft analyzer for sulfur content estimation. The ARDPPLS takes complicated characteristics of the hydrogenation process into consideration, including process dynamics, nonlinearities, and transportation delays of materials and energies. Specifically, the dynamics and nonlinearities are modeled by the finite impulse response paradigm and polynomial fitting, respectively, and the delays of explanatory variables are automatically determined using differential evolution. In particular, a Bayesian inference‐based adaptive regularization scheme for the polynomial partial least squares is designed to tackle overfitting that results from high‐order variable augmentation and high‐order polynomials. The performance of the ARDPPLS is evaluated by a real‐world industrial wax oil hydrocracking process, showing the effectiveness of the ARDPPLS.

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Smart optimization with PPCR modeling in the presence of missing data, time delay and model-plant mismatch

Memarian

Varanasi

Huang

et al. 2023

Chemometrics and Intelligent Laboratory Systems

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Robust probabilistic principal component regression with switching mixture Gaussian noise for soft sensing

Cited by 10 publications

References 20 publications

Virtual Sensing of Key Variables in the Hydrogen Production Process: A Comparative Study of Data-Driven Models

Virtual Sensing of Key Variables in the Hydrogen Production Process: A Comparative Study of Data-Driven Models

Soft analyzer for sulfur content in tail oil of hydrocracking process based on adaptively regularized dynamic polynomial PLS

Smart optimization with PPCR modeling in the presence of missing data, time delay and model-plant mismatch

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