Soft measurement model and its application in raw meal calcination process

Qiao, Jinghui; Chai, Tianyou

doi:10.1016/j.jprocont.2011.08.005

Cited by 32 publications

(21 citation statements)

References 20 publications

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“…The high-temperature raw material is driven into the cyclone C4 by heat exhaust gas flow and then fed into the calciner. In the calciner, the raw material is decomposed and precalcined with the decomposition rate of 85-95% [19]. Finally, the precalcined raw material is fed into the rotary kiln.…”

Section: Description Of the Cement Raw Materials Calcination Processmentioning

confidence: 99%

2-D regularized locality preserving projection algorithms for temporospatial feature reduction and its application in industrial data regression

2015

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Section: Description Of the Cement Raw Materials Calcination Processmentioning

confidence: 99%

2-D regularized locality preserving projection algorithms for temporospatial feature reduction and its application in industrial data regression

2015

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“…From (1), it can be seen that RMDR γ(t) is a nonlinear function of the calciner temperature y 1 , the preheater C1 outlet temperature y 2 and varying with the boundary conditions B. To sove this problem, a soft measurement model based on RFMPCA and LS-SVM is established in [12]. The auxiliary variable set of this model are listed in [12].…”

Section: ) Algorithm For Control Loop Pre-setting Modelmentioning

confidence: 99%

“…To sove this problem, a soft measurement model based on RFMPCA and LS-SVM is established in [12]. The auxiliary variable set of this model are listed in [12]. The RMDR value can be on-line calculated to obtain a feedforward compensation control.…”

Section: ) Algorithm For Control Loop Pre-setting Modelmentioning

confidence: 99%

Intelligent setting control of raw meal calcination proces

Qiao

Chai

Wang

2011

IEEE Conference on Decision and Control and European Control Conference

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Abstract-In raw meal calcination process, the target value of decomposition ratio of raw meal (RMDR) is different in easy calcination stage and difficult calcination stage because boundary conditions of raw meal change frequently, where RMDR cannot be guaranteed within its desirable ranges. To solve this problem, an intelligent setting control method is proposed. This method for raw meal calcination process consists of five modules, namely a RMDR target value setting model using subtraction clustering method (SCM) and adaptivenetwork-based fuzzy inference system containing categorical input (C-ANFIS), a control loop pre-setting model, a feedback compensation model based on fuzzy rules, a feedforward compensation model based on fuzzy rules, and a soft measurement model for RMDR. The proposed method is realized by on-line adjusting the setpoints of control loops with the change of raw meal boundary conditions. This method has been successfully applied to the raw meal calcination process of Jiuganghongda Cement Plant in China and its efficiency has been validated by the practical application results.

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“…Soft-sensors, which are actually model-based inferential estimators, have been successfully employed in mineral processing industries as well as many other process industrials, such as petrochemical, biotechnological, and pulp and paper [2]- [11]. The foundational principle of soft-sensor is to infer the immeasurable primary variables (most likely the product quality indices which are difficult to be measured online) by using some secondary variables (SVs) with the help of an appropriate mathematical model of the process.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, periodical offline learning for the ANN soft-sensor is adopted in most cases. However, this offline learning manner requires a large amount of data and human involving, so it is unconventional to be implemented in practice [11], [13].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Soft-Sensor Modeling for Product Quality Estimation Using Case-Based Reasoning and Fuzzy-Similarity Rough Sets

Zhou

Chai

2014

IEEE Trans. Automat. Sci. Eng.

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Efficient operation of the integrated optimization or automation system in an industrial plant depends mainly on good measurement of product quality. However, measuring or estimating the product quality online in many industrial plants is usually not feasible using the available techniques. In this paper, a data-driven soft-sensor using case-based reasoning (CBR) and fuzzy-similarity rough sets is proposed for product quality estimation. Owning to the sustained learning ability, the modeling of a CBR soft-sensor does not need any additional model correction which is otherwise required by the neural network based methods to overcome the slow time-varying nature of industrial processes. Because the conventional -nearest neighbor ( -NN) algorithm is strongly influenced by the value of , an improved -NN algorithm with dynamic adjustment of case similarity threshold is proposed to retrieve sufficient matching cases for making a correct estimation. Moreover, considering that the estimation accuracy of the CBR soft-sensor system is closely related to the weights of case feature, a feature weighting algorithm using fuzzy-similarity rough sets is proposed in this paper. This feature weighting method does not require any transcendental knowledge, and its computation complexity is only linear with respect to the number of cases and attributes. The developed soft-sensor system has been successfully applied in a large grinding plant in China. And the application results show that the system has achieved satisfactory estimation accuracy and adaptation ability.Note to Practitioners-In the process industry, the product quality reflects the operational and economic performance of a manufacturing process. However, online measurement and control of product quality is generally difficult. In this paper, a data-driven soft-sensor is proposed to online estimate the product quality for a typical grinding process with time-varying dynamics. The method combines an improved CBR soft-sensor algorithm and a feature weighting algorithm using fuzzy-similarity rough sets. First, the weighs of case features in CBR are determined using the fuzzy-similarity rough sets in offline mode, and then they are used in CBR to accurately estimate the PPS online. The CBR soft-sensor system has been applied in a large industrial grinding plant. The application results show that the soft-sensor system has achieved satisfied estimation precision, good adaptability and Manuscript robustness, and can be easily realized in practice with low cost and maintenance.Index Terms-Case feature weighting, case-based reasoning (CBR), data-driven, fuzzy-similarity rough sets (FSRS), product quality estimation, soft-sensor modeling.

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Soft measurement model and its application in raw meal calcination process

Cited by 32 publications

References 20 publications

2-D regularized locality preserving projection algorithms for temporospatial feature reduction and its application in industrial data regression

2-D regularized locality preserving projection algorithms for temporospatial feature reduction and its application in industrial data regression

Intelligent setting control of raw meal calcination proces

Data-Driven Soft-Sensor Modeling for Product Quality Estimation Using Case-Based Reasoning and Fuzzy-Similarity Rough Sets

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