Design of Neural Network Based Expert System for Automated Lime Kiln System

Anand, S. Krishna; Mamatha, E.; Reddy, C. S.; Prabha, M. Lakshmi

doi:10.18280/jesa.520406

Cited by 15 publications

(14 citation statements)

References 25 publications

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“…This has been illustrated in Figure 8. It has been observed that there is a 25% reduction in emission of equivalent Carbon over a three-year period [22].…”

Section: Resultsmentioning

confidence: 99%

“…In the later part of the year, Lan [21] also considered an agricultural application and analyzed the sensitivity of consumer's greenness sensitivity, green cost coefficient and stochastic output ratio. During the same year, Anand et al [22] had brought into limelight emissions of greenhouse gases during the design of a neural network for an automated lime kiln plant.…”

Section: Literature Surveymentioning

confidence: 99%

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Incorporation of Innovative Mechanisms for Greenhouse Gas Emission Reduction

Anand¹,

Raman²

2021

EESRJ

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Spiraling continued increase in Emission of Green House Gases [GHG] play a significant role in impacting the environment and also human beings at large. Although recent studies have concentrated to an extent on developing schemes for reduction of Carbon dioxide emission and have identified methodologies in implementing the same, sufficient amount of studies have not been done on other greenhouse gases which also have adverse global impact as Carbon dioxide. Applications where methane and, nitrous oxide are emitted in abundance have continued to flourish. This work focuses on select methodologies in reducing all types of Greenhouse gases giving a larger amount of importance to ones which are more severe and the ones that cause depletion of ozone layer. Research findings have shown that majority of greenhouse gas emissions occur as a result of industry advances. Hence, as time is running out, there is an urgent need in identifying ways to mitigate these GHG emissions, thereby contributing to cleaner and healthier environment.

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“…This has been illustrated in Figure 8. It has been observed that there is a 25% reduction in emission of equivalent Carbon over a three-year period [22].…”

Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Incorporation of Innovative Mechanisms for Greenhouse Gas Emission Reduction

Anand¹,

Raman²

2021

EESRJ

Self Cite

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“…A research group used AI to optimize energy consumption and throughput of cement kilns and mills by improving equipment productivity as well as reducing CO 2 emissions. [7] Anand et al [10] showed that the shell temperature of a lime kiln and furnace oil consumption are two key parameters to the working efficiency and product quality of the kiln. So, they designed intelligent controllers based on artificial neural network to optimize the parameters in kiln application.…”

Section: Related Workmentioning

confidence: 99%

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Ali

Tabares

McGinley

2022

J Adv Manuf & Process

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Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

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“…Goense compared autonomous with conventional vehicles, equipped with implements having working widths from 50 to 120 cm. He showed, if autonomous vehicles are often utilized 23 hours every day, it'd be economically feasible with slight reductions in prices of navigation systems or with slight increases parturient costs [21]. On the other hand, there could even be negative effects within the type of upper costs in traveling distances for service personal.…”

Section: Economics Aspectmentioning

confidence: 99%

Scientific Trends and Role of Robots in the Agricultural Sector

Mamatha¹,

Saritha²,

Anand³

et al. 2020

IJANA

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The agriculture sector is essential to implement the automation technology to grow rapidly for better results and to produce crops for the requirement of the world population in the present scenario. Present existing technology and old mythologies and tools and types of machinery are not sufficient to achieve the targets. The enhanced technology and optimized energy utilization tools are straightaway to be adopted in the form of information technology machines that are more effective with old methods. The precision formation has revealed the advantages of machinery technology but needs to improve more for the next new equipment for the coming generations, which we call robotics technologies. The initiation of an autonomous system gives us a better prospect to build up a completely new variety of farming kits with the support of small elegant equipments like types of machinery which will do an accurate process, at the right time with the exact place, in the correct approach. A machine that contains an automatic control mechanism tracks the front running vehicle and follows, which has advantages of both vehicles that can be controlled and monitored effectively by a single driver in large farming sectors.

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Design of Neural Network Based Expert System for Automated Lime Kiln System

Cited by 15 publications

References 25 publications

Incorporation of Innovative Mechanisms for Greenhouse Gas Emission Reduction

Incorporation of Innovative Mechanisms for Greenhouse Gas Emission Reduction

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Scientific Trends and Role of Robots in the Agricultural Sector

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