Modelling and control of chaotic processes through their bifurcation diagrams generated with the help of recurrent neural network models. Part 2: An industrial study

Krishnaiah, J.; Kumar, C.S.; Faruqi, M.A.

doi:10.1016/j.jprocont.2005.04.003

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…The union of all these interconnected neurons the artificial neural network [50], [51], [54], [55]. The artificial neural network as well as biological networks learn by repetition, and the more data you must train and the more times you train the network the better results you will get [62], [63], [67]. Training an ANN is a process that modifies the value of the weights associated with each neuron, so that the ANN can generate an output from the data presented in the input [52].…”

Section: Methodsmentioning

confidence: 99%

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Alarcón¹,

Rivera-M²,

Golondrino³

2022

IJACSA

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The concentrate process is the most sensitive in mineral processing plants (MPP), and the optimization of the process based on intelligent computational models (machine learning for recovery percentage modelling) can offer significant savings for the plant. Recent theoretical developments have revealed that many of the parameters commonly assumed as constants in gravity concentration modelling have a dynamic nature; however, there still lacks a universal way to model these factors accurately. This paper aims to understand the model effect of operational parameters of a jig (gravimetric concentrator) on the recovery percentage of the interest mineral (gold) through empirical modeling. The recovery percentage of mineral particles in a vibrated bed of big particles is studied by experimental data. The data used for the modelling were from experimental test in a pilot-scale jig supplemented by a twomonth field sampling campaign for collecting 151 tests varying the most significant parameters (amplitude and frequency of pulsation, water flow, height of the artificial porous bed, and particle size). It is found the recovery percentage (%R) decreases with increasing pulsation amplitude (A) and frequency (F) when the size ratio of small to large particles (d/D) is smaller than 0.148. An empirical model was developed through machine learning techniques, specifically an artificial neural network (ANN) model was built and trained to predict the jig recovery percentage as a function of operation parameters and is then used to validate the recovery as a function of vibration conditions. The performance of the ANN model was compared with a new 65 experimental data of the recovery percentage.Results showed that the model (R 2 = 0.9172 and RMSE = 0.105) was accurate and therefore could be efficiently applied to predict the recovery percentage in a jig device.

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Section: Methodsmentioning

confidence: 99%

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Alarcón¹,

Rivera-M²,

Golondrino³

2022

IJACSA

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“…It can be clearly interpreted from the mathematical definition that: a direct mathematical concept is O ¼ f (I), and a reverse mathematical concept is O ¼ f À1 (I), where the O denotes the system's output, I denotes the system's input, and f will be mathematical relationship between O and I. The neural networks can be used to model this logical analysis to achieve quality optimization [19][20][21][22]31]. In this study, we also apply the logical analysis mentioned to construct the procedure and make modification.…”

Section: Proposed Approachmentioning

confidence: 99%

“…Artificial neural networks (ANNs) are mentioned to be applied in process modeling problems [19][20][21][22]. Tong and Hsieh [23] had proposed a novel means of applying ANN to solve the multi-response optimization combining the quantitative and qualitative response.…”

Section: Introductionmentioning

confidence: 99%

Multi-response Problem with Adaptive Weight Consideration by Using Weight Aggregator and ANNs

Hsieh

2009

Concurrent Engineering

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This study proposes a procedure incorporating artificial neural networks technique with adaptive weight consideration to address parameter optimization of a multiple responses problem. No matter what type of the experimental designs being employed, the proposed approach can be directly employed. Besides, the consistency and difference among several evaluations among those multiple responses can also be studied via the designed aggregation weight values in our proposed procedure. An illustrative example owing to the lead frame manufacturer in Taiwan is also employed to demonstrate the feasibility and rationality of the proposed procedure.

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“…Electric arc furnaces are used to produce FeSi by melting the raw materials (mainly quartz, coal, and coke) using electrical supply as main energy input [1][2][3][4][5][6]. The electrode system, connected to the electrical supply as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Fuzzy Pid Controllers for a Submerged Arc Ferrosilicon Furnace

Shabib

2012

JES. Journal of Engineering Sciences

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This paper investigates the control strategy for the electrical energy input to three phase electric arc ferrosilicon furnace. The classical PD fuzzy logic controller has two inputs. These inputs are the error in the V-I characteristic and the rate of the error in V-I characteristic. When the simulation result of this controller is compared with conventional PD type, it was shown that requires steady state accuracy. This paper is concerned with incorporating the integral property to the classic fuzzy PD controller. The result is a nonlinear fuzzy PID controller eliminates the steady state error resulted from the classic fuzzy PD actions. In this paper, the submerged arc ferrosilicon furnace is described by the heat transfer system, the electrical system equipments and the dynamics from the electrode positions to the electrode currents. The proposed control design methodology is tested on the model of submerged arc ferrosilicon furnace FeSi via simulation. The simulation work is carried out using simple environment via computer program written using C++ language. The obtained results guarantee the potential of the proposed control methodology to add self autonomy to the system behavior.

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Modelling and control of chaotic processes through their bifurcation diagrams generated with the help of recurrent neural network models. Part 2: An industrial study

Cited by 7 publications

References 12 publications

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Multi-response Problem with Adaptive Weight Consideration by Using Weight Aggregator and ANNs

Modeling and Simulation of Fuzzy Pid Controllers for a Submerged Arc Ferrosilicon Furnace

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