Automatic Setting Control of the Raw Slurry Blending Process Based on the Case-Based Reasoning

2011 ◽  
Vol 299-300 ◽  
pp. 667-670 ◽  
Author(s):  
Lun Hai Yin ◽  
Rui Bai ◽  
Wan Li Guo
Keyword(s):  
Control Method ◽  
Raw Materials ◽  
Optimal Operation ◽  
Case Based Reasoning ◽  
Process Data ◽  
Alumina Production ◽  
Control Objective ◽  
Operation Control ◽  
Blending Process ◽  
Case Based

Raw slurry blending process is the key process in the sintering alumina production. In this blending process, raw materials are alkali powder, red mud, blending ore and limestone, and the product is the raw slurry. The optimal operation control objective of this blending process is to make the quality indices of the raw slurry into their targeted ranges. The key step to realize this control objective is to decide the appropriate set-points of the control loop. An automatic setting control method is proposed in this paper. During the setting process, case-based reasoning is adopted to obtain the appropriate set-points of the control loops according to the process data and state. By using this setting control method, appropriate set-points can be obtained and the operation control objectives can be realized.

Research on the Air-Fuel Ratio Intelligent Control Method for Coke Oven Combustion Energy Saving

2011 ◽  
Vol 121-126 ◽  
pp. 2873-2877 ◽  
Author(s):  
Gong Fa Li ◽  
Yuan He ◽  
Guo Zhang Jiang ◽  
Jian Yi Kong ◽  
Liang Xi Xie
Keyword(s):  
Energy Saving ◽  
Working Conditions ◽  
Gas Flow ◽  
Control Method ◽  
Combustion Process ◽  
Coke Oven ◽  
Case Based Reasoning ◽  
Fuel Ratio ◽  
Intelligent Technology ◽  
Case Based

Coke combustion process, the constant proportion of the combustion air-fuel ratio control results in low combustion efficiency and fault-prone, difficult to adapt to changes in complex working conditions. Application of intelligent technology of case-based reasoning, fuzzy control, proposed for intelligent energy saving air-fuel ratio control method. Based on current trends in working conditions and combustion process in case of failure, predict the typical faults with case-based reasoning technology to the combustion process. On this basis, through case-based reasoning algorithm realize the real-time air-fuel ratio correction. Based on fuzzy-PID temperature cascade control we can obtain the appropriate flue gas flow and flue suction and realize the stability of the combustion process to achieve optimal control.

scholarly journals The Decision Support System in the Domain of Casting Defects Diagnosis

2014 ◽  
Vol 14 (3) ◽  
pp. 107-110 ◽  
Author(s):  
D. Wilk-Kołodziejczyk ◽  
G. Rojek ◽  
K. Regulski
Keyword(s):  
Decision Support ◽  
Computer System ◽  
Expert Knowledge ◽  
Opportunity To Learn ◽  
Case Based Reasoning ◽  
Casting Defects ◽  
Process Data ◽  
Support Tool ◽  
Implementation Phase ◽  
Case Based

Abstract This article presents a computer system for the identification of casting defects using the methodology of Case-Based Reasoning. The system is a decision support tool in the diagnosis of defects in castings and is designed for small and medium-sized plants, where it is not possible to take advantage of multi-criteria data. Without access to complete process data, the diagnosis of casting defects requires the use of methods which process the information based on the experience and observations of a technologist responsible for the inspection of ready castings. The problem, known and studied for a long time, was decided to be solved with a computer system using a CBR (Case-Based Reasoning) methodology. The CBR methodology not only allows using expert knowledge accumulated in the implementation phase, but also provides the system with an opportunity to “learn” by collecting new cases solved earlier by this system. The authors present a solution to the system of inference based on the accumulated cases, in which the main principle of operation is searching for similarities between the cases observed and cases stored in the knowledge base.

scholarly journals An Improved Case-Based Reasoning Model for Simulating Urban Growth

2021 ◽  
Vol 13 (11) ◽  
pp. 6146
Author(s):  
Xin Ye ◽  
Wenhui Yu ◽  
Lina Lv ◽  
Shuying Zang ◽  
Hongwei Ni
Keyword(s):  
Urban Growth ◽  
Urban Areas ◽  
Control Method ◽  
Growth Models ◽  
Case Based Reasoning ◽  
Initial State ◽  
Simulation Accuracy ◽  
Complex Dynamic Systems ◽  
Time Control ◽  
Case Based

Developing urban growth models enables a better understanding and planning of sustainable urban areas. Case-based reasoning (CBR), in which historical experience is used to solve problems, can be applied to the simulation of complex dynamic systems. However, when applying CBR to urban growth simulation, problems such as inaccurate case description, a single retrieval method, and the lack of a time control mechanism limit its application accuracy. In order to tackle these barriers, this study proposes a CBR model for simulating urban growth. This model includes three parts: (1) the case expression mode containing the “initial state-geographical feature-result” is proposed to adapt the case expression to the urban growth process; (2) in order to improve the reliability of the results, we propose a strategy to introduce the “retrieval quantity” parameter and retrieve multiple similar cases; and (3) a time factor control method based on demand constraints is proposed to improve the power of time control in the algorithm. Finally, the city of Jixi was used as the study area for simulation, and when the “retrieval quantity” is 10, the simulation accuracy reaches 97.02%, kappa is 85.51, and figure of merit (FoM) is 0.1699. The results showed that the proposed method could accurately analyze urban growth.

Multivariate Principal Component Analysis and Case-Based Reasoning for monitoring, fault detection and diagnosis in a WWTP

2011 ◽  
Vol 64 (8) ◽  
pp. 1661-1667 ◽  
Author(s):  
Magda Ruiz ◽  
Gürkan Sin ◽  
Xavier Berjaga ◽  
Jesús Colprim ◽  
Sebastià Puig ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Treatment Plant ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Detection And Diagnosis ◽  
Case Based Reasoning ◽  
Process Data ◽  
Predictive Diagnosis ◽  
Case Based

The main idea of this paper is to develop a methodology for process monitoring, fault detection and predictive diagnosis of a WasteWater Treatment Plant (WWTP). To achieve this goal, a combination of Multiway Principal Component Analysis (MPCA) and Case-Based Reasoning (CBR) is proposed. First, MPCA is used to reduce the multi-dimensional nature of online process data, which summarises most of the variance of the process data in a few (new) variables. Next, the outputs of MPCA (t-scores, Q-statistic) are provided as inputs (descriptors) to the CBR method, which is employed to identify problems and propose appropriate solutions (hence diagnosis) based on previously stored cases. The methodology is evaluated on a pilot-scale SBR performing nitrogen, phosphorus and COD removal and to help to diagnose abnormal situations in the process operation. Finally, it is believed that the methodology is a promising tool for automatic diagnosis and real-time warning, which can be used for daily management of plant operation.

scholarly journals Integrated Modeling and Intelligent Control Methods of Grinding Process

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Jie-sheng Wang ◽  
Na-na Shen ◽  
Shi-feng Sun
Keyword(s):  
Intelligent Control ◽  
Control Method ◽  
Soft Sensor ◽  
Integrated Modeling ◽  
Case Based Reasoning ◽  
Grinding Process ◽  
Control Methods ◽  
Set Point ◽  
Self Tuning ◽  
Case Based

The grinding process is a typical complex nonlinear multivariable process with strongly coupling and large time delays. Based on the data-driven modeling theory, the integrated modeling and intelligent control method of grinding process is carried out in the paper, which includes the soft-sensor model of economic and technique indexes, the optimized set-point model utilizing case-based reasoning, and the self-tuning PID decoupling controller. For forecasting the key technology indicators (grinding granularity and mill discharge rate of grinding process), an adaptive soft-sensor modeling method based on wavelet neural network optimized by the improved shuffled frog leaping algorithm (ISFLA) is proposed. Then, a set point optimization control strategy of grinding process based on case-based reasoning (CBR) method is adopted to obtain the optimized velocity set-point of ore feed and pump water feed in the grinding process controlled loops. Finally, a self-tuning PID decoupling controller optimized is used to control the grinding process. Simulation results and industrial application experiments clearly show the feasibility and effectiveness of control methods and satisfy the real-time control requirements of the grinding process.

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