Abstract:Boiler efficiency is called to some extent of total thermal energy which can be recovered from the fuel. Boiler efficiency losses are due to four major factors: Dry gas flux, the latent heat of steam in the flue gas, the combustion loss or the loss of unburned fuel, and radiation and convection losses. In this research, the thermal behavior of boilers in gas refinery facilities is studied and their efficiency and their losses are calculated. The main part of this research is comprised of analyzing the effect o… Show more
“…2 shows graphic representation of dataset split. The test data is not used in the training phase but only to evaluate the performance and examine the quality of the ML model [ 19 ]. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…Various activation functions have been applied to build DNNs, such as Sigmoid , Tanh , Softplus , and ReLu . Today, better performance is seen using the ReLu activation function [ 19 ].…”
“…2 shows graphic representation of dataset split. The test data is not used in the training phase but only to evaluate the performance and examine the quality of the ML model [ 19 ]. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…Various activation functions have been applied to build DNNs, such as Sigmoid , Tanh , Softplus , and ReLu . Today, better performance is seen using the ReLu activation function [ 19 ].…”
“…Further, the effect of each variable on the boiler combustion control system is analyzed wherein they suggested measures to improve the performance. Maddah et al (2019) recommend the neural network and response‐surface method‐based framework to analyze the effect of various parameters on boiler efficiency. The results show the possibility of the analysis and give the recommendations for optimizing boilers in complex refinery.…”
Section: Introductionmentioning
confidence: 99%
“…Further, the effect of each variable on the boiler combustion control system is analyzed wherein they suggested measures to improve the performance. Maddah et al (2019) volutional neural network model is introduced for complex industry data prediction. The results obtained show that the adaptability and stability of the CAN method is higher than the traditional wavelet transform denoising and denoising autoencoders.…”
The implementation of Industry 4.0 needs the convergence in operational technology and information technology (OT–IT) for improving the flexibility to access real‐time data and KPIs. In India, a small‐scale automation industry is full of edge devices and produces massive amount of data due to which an infrastructure may overwhelm. These industries face challenges in convergence of IT–OT like scalability. This work developed a data‐driven framework, which is an additional infrastructure between the IT–OT platforms. The framework consists of data cleaning which helps to remove the anomaly from the sensor data. The uncertainty analysis ensures the correctness of the data. Variable ranking and correlation matrix help to identify the KPIs of the process. All these stages are helpful to select the data and push this selected data to cloud for developing predictive or diagnostic models. This framework is deployed for the performance prediction of four‐effect falling‐film evaporator used to produce skimmed milk. The outlet skimmed milk density is the key performance indicator for the present case study and it is monitored here. Both linear and neural network‐based nonlinear models are used in the framework. It is noticed that linear regression model is capable of predicting the plant product density of skimmed milk with good accuracy. However, it is not capable to identify the faults in the key parameters like inlet milk flow rate. These faults can be better identified using k‐means clustering at the initial stage of the framework.
Practical applications
This article titled “A data‐analytic framework to monitor product density of four‐effect falling‐film evaporator for skimmed milk production” by Nivedita Wagh and Sudhir D. Agashe contains the results obtained for data‐driven framework for performance prediction and fault diagnosis for four‐effect falling‐film evaporator used for production of skimmed milk. A data‐driven framework which includes uncertainty analysis, variable ranking, various linear and neural network‐based nonlinear regression models for performance prediction, and fault detection of an evaporator is proposed. The results are presented in terms of density of the skimmed milk as a performance parameter. The objective of the work is to provide the possibility of IT–OT convergence so as to solve the scalability issue in the IT–OT convergence. The authors are able to bring out a data‐driven framework for fault identification using k‐means clustering using four‐effect falling‐film evaporator used for production of skimmed milk as a case study. We hope the results will be useful to data managers for understanding the KPIs statistics and take appropriate decision in time for the overall growth of organization.
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