Dec 18, 2007 View more. Grinding circuit must provide stable particle size distribution and should also operate in a way to maximize mill efficiency. Fuzzy logic based on-line optimization control integrated in an expert system was developed to control product particle size while enhancing mill efficiency in a ball mill grinding circuit. In the supervisory level
Several intelligent controllers designed based on fuzzy logic [13][14][15][16] for the cement ball mill grinding process were able to track the setpoint and reject the disturbance better than the
cement mill for further grinding. 3. FUZZY LOGIC CONTROLLER Fuzziness means vagueness. Fuzzy set theory is an excellent tool to handle the uncertainty arising due to vagueness. Fuzzy logic control system is better than the PID control system in terms of robustness and less
Optimal Design of MIMO-Fuzzy Logic Controller using Convergent Heterogeneous Particle Swarm Optimization for a Cement Mill R. Krishna Priya, P.S. Godwin Anand and Susamma Chacko Abstract: Optimal design of a Fuzzy logic controller has traditionally been achieved through a process of trial and error
membership function of a MIMO fuzzy logic control for a nonlinear model of the cement mill circuit is presented. The optimization is done by GA based on minimization on Integral absolute error (IAE) of finished product yf and mill level z. The performance of the proposed control scheme is tested for different
Feb 15, 2021 The cycloconverter output voltage and frequency can be changed uninterruptedly using a fuzzy logic based control circuit. In this chapter, the fuzzy logic controller is used to control the output of the converter. This work shows how to get variable voltage and frequency to control the cement mill drives with the help of a fuzzy-based controller
Control System Architecture for a Cement Mill Based on Fuzzy Logic 169 Figure 6: Control surface Figure 7: Graphical construction of the control signal in a fuzzy controller 170 C.R. Costea, H.M. Silaghi, D. Zmaranda, M.A. Silaghi 3 The mill control system structure A control system based on PLCs for clinker grinding circuit is developed
Jan 07, 2016 Development of Fuzzy Logic Controller for Cement Mill - written by Stephy Retnam, Pratheesh H, Aswin R. B published on 2016/07/01 download full article with reference data and citations
In this study, artificial neural networks (ANN) and fuzzy logic models were developed to model relationship among cement mill operational parameters. The response variable was weight percentage of product residue on 32-micrometer sieve (or fineness), while the input parameters were revolution percent, falofon percentage, and
Aug 01, 1981 Japan, 1981 CONTROL OF A CEMENT KILN BY FUZZY LOGIC TECHNIQUES L. P. Holmblad and J-J. Ostergaard F. L. Smidth (I Co. A/S, Vigerslev Alle 77, DK-2500 Valby, Denmark Abstract. By applying the methodology of fuzzy logic the operat10nal experience of manual control can be used as the basis for implementing automatic control schemes
Fuzzy logic based on-line optimization control integrated in an expert system was developed to control product particle size while enhancing mill efficiency in a ball mill grinding circuit. In the supervisory level, fuzzy logic control determined the optimum set-points for the controllers in the regulatory level
Fuzzy logic is a mathematical theory of inexact reasoning that allows modeling of the reasoning process of humans in linguistic terms, and it is an effective method approved to deal with the fuzzy objects in many areas of engineering application. The purpose of this paper is to explore the relationship between grinding process parameters and surface quality parameters of
mills). New energy management systems that use artificial intelligence, fuzzy logic (neural network), or rule-based systems mimic the ―best‖ controller, using monitoring data and learning from previous experiences. Process knowledge based systems (KBS) have been used in design and diagnostics, but are hardly used in industrial processes
Advanced process control & analytics optimize cement kiln operation, alternative fuel use, mills and blending How advanced process control (APC) and related optimization strategies can help cement manufacturers to reap the real efficiency benefits of digital technology, without sacrificing stability or quality, even as a business changes and grows
Vertical Roller Mill Cement Raw Material Grinding. Raw Material Drying-Grinding - Cement Plant Optimization. Vertical roller mills can typically handle an aggregate moisture of up to 20 in raw materials and consumes about 30 less power in grinding. Hence it is commonly preferred for grinding operation in new plants of higher capacities
This paper is a case study that describes a hybrid system integrating fuzzy logic, neural networks and algorithmic optimization for use in the ceramics industry. A prediction module estimates two quality metrics of slip-cast pieces through the simultaneous execution of two neural networks. A process improvement algorithm optimizes controllable process settings using the neural
Mill Process Optimization: Grinding makes up a big portion of the electrical energy consumed on the plant, thus the efficiency of grinding operations has a big influence on your energy bill. SMARTA APC Suite optimizes your grinding circuit to increase throughput and secure consistent output quality while lowering energy consumption
Dedicated to control cement mill. The application of fuzzy logic and expert systems to control the difference shown in the control system using fuzzy regulators for the operation of the grinding without unnecessary stops, it also helps the operator to know the maintenance task to perform
Nov 24, 2020 DOI: 10.1080/14680629.2020.1847726 Corpus ID: 229514409; Application of fuzzy logic and grey based Taguchi approach for additives optimization in expansive soil treatment @article{Ikeagwuani2020ApplicationOF, title={Application of fuzzy logic and grey based Taguchi approach for additives optimization in expansive soil treatment}