Pulp and paper manufacture

The digital transformation (DT) is the acquiring the digital tool, techniques, approaches, mechanism etc. for the transformation of the business, applications, services and upgrading the manual process into the automation. The DT enable the efficacy of the system via automation, innovation, creativities. The another concept of DT in the engineering domain is to replace the manual and/or conventional process by means of automation to handle the big-data problems in an efficient way and harness the static/dynamic system information without knowing the system parameters. The DT represents the both opportunities and challenges to the developer and/or user in an organization, such as development and adaptation of new tool and technique in the system and society with respect to the various applications (i.e., digital twin, cybersecurity, condition monitoring and fault detection & diagnosis (FDD), forecasting and prediction, intelligent data analytics, healthcare monitoring, feature extraction and selection, intelligent manufacturing and production, future city, advanced construction, resilient infrastructure, greater sustainability etc.). Additionally, due to high impact of advanced artificial intelligent, machine learning and data analytics techniques, the harness of the profit of the DT is increased globally. Therefore, the integration of DT into all areas deliver a value to the both users as well as developer. In this editorial fifty two different applications of DT of distinct engineering domains are presented, which includes its detailed information, state-of-the-art, methodology, proposed approach development, experimental and/or emulation based performance demonstration and finally conclusive summary of the developed tool/technique along with future scope.

Digital transformation through advances in artificial intelligence and machine learning

The practical significances and complexities of financial time series analysis induce highly demand more reliable hybrid model that denoised the data efficiently, handled with both linear and nonlinear patterns in the data, to achieve more accurate results. This paper suggests a new forecasting hybrid model for financial time series data combined Empirical Wavelet Transform (EWT) technique with improved Artificial Bee Colony (ABC) algorithm, Extreme Learning Machine (ELM) neural network, and Auto-Regressive Integrated Moving Average (ARIMA) linear analysis algorithm. The EWT is used to decompose and denoise the data to reconstruct the data more suitable for forecast. The improvement of the ABC algorithm is according to the Good Point Sets (GPS) theory and adaptive Elite-based Opposition (EO) strategy (GPS-EO-ABC) to overcome the drawbacks of the original algorithm and enhance the optimization performance. The optimized ELM with GPS-EO-ABC, as well as the ARIMA, are utilized independently to generated different forecasting results and combined by the weight-based procession. We testify the performance of the proposed improved ABC algorithm by ten benchmark functions, simulating the proposed forecasting models by three financial time-series datasets. The results indicate that: (1) The proposed algorithm shows outstanding capacities in parameter optimization. The optimized ELM generated more stable and precise results compared with original ELM, ABC-ELM, single LSTM, and ANN; (2) The proposed hybrid model has not only effectiveness but also efficiencies in denoising data, correcting outliers, coordinating both linear and nonlinear patterns, its performance in financial time series forecasting is more excellent than existing hybrid models.

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A Hybrid Model for Financial Time Series Forecasting—Integration of EWT, ARIMA With The Improved ABC Optimized ELM

Design & application of adaptive variable structure & H∞ robust optimal schemes in nonlinear control of boiler-turbine unit in the presence of various uncertainties

A maximum likelihood (ML) estimator for digital sequences disturbed by Gaussian noise, intersymbol interference (ISI) and interchannel interference (ICI) is derived. It is shown that the sampled outputs of the multiple matched filter (MMF) form a set of sufficient statistics for estimating the input vector sequence. Two ML vector sequence estimation algorithms are presented. One makes use of the sampled output data of the multiple whitened matched filter and is called the vector Viterbi algorithm. The other one is modification of the vector Viterbi algorithm and uses directly the sampled output of the MMF. It appears that, under a certain condition, the error performance is asymptotically as good as if both ISI and ICI were absent.

Maximum likelihood receiver for multiple channel transmission systems

The analysis of the lockdown effect during covid-19 pandemic on ambient quality of air of Uttarakhand state of India, has been performed. The combination of SO 2 , NO 2 , and particulate matter (P.M.10) indicates ambient air quality characteristics. The clustering capability of the K-means clustering technique is investigated with two different approaches of measuring distance using MATLAB. The first approach is termed Euclidean distance and the second one is cosine distance. The data, which is clustered, is the air uualitv data containing three major components of air pollution such as P.M.10, SO 2 , and NO 2 of different major cities of Uttarakhand.

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K-Means Clustering of Ambient Air Quality Data of Uttarakhand, India during Lockdown Period of Covid-19 Pandemic

The input to the sugar factory is the sugarcane billets and the output is the crystal sugar. There are many subprocesses with significant multivariable interaction involved within this process which is cane preparation, juice extraction by crushing mill, heating, clarification and filtration, evaporation, and crystallization. In the present paper, the Smith predictor is designed using MATLAB in order to compensate the dead time present in heat exchanger system of the crystallization process and its performance is compared to that of a conventional PI controller with no dead time compensation.

Dead Time Compensation in Sugar Crystallization Process

System identification refers to finding out an exact mathematical model, of a process with unknown characteristics, relating input and output. A control system can only be designed for a process when its mathematical model in any form is available. In the present work, the cane carrier system of sugar mill is considered and its various mathematical models are obtained from the measured time domain input-output data using MATLAB.

System Identification of Cane Carrier Process of Sugar Mill

In sugar factory there are a number of processes involved out of which, the crushing mill process which extracts the juice from sugarcane fibers is the key process which is complex and nonlinear in nature. For maximum juice extraction, maintaining the cane level in the chute at an optimum level is essential. The manipulated variable for controlling cane level is the speed of cane carrier motor. In the present paper, three different control strategies namely PID, fuzzy and prediction based control for maintaining the cane level are presented and their performance is compared.

Control of sugarcane crushing mill process: A comparative analysis

Several industrial chemical processes are found tobe FOPDT (first order plus dead time). The presence of deadtime makes the transfer function to be irrational and makesthe simulation and controller design very complex. In thepresent work an FOPDT model of the pH neutralizationprocess which is found in sugar mills is considered as a subjectof study and analysis. Having obtained its Padeapproximation, its order has been reduced using Square RootBalance Truncation Method and then for this reduced ordermodel, Ziegler-Nichols(ZN), Internal model control (IMC)and optimized IMC using Genetic Algorithm(GA-IMC)based controllers are designed and their performance is compared.

Sandeep Kumar Sunori

Papers

K-Means Clustering of Ambient Air Quality Data of Uttarakhand, India during Lockdown Period of Covid-19 Pandemic

Dead Time Compensation in Sugar Crystallization Process

System Identification of Cane Carrier Process of Sugar Mill

Control of sugarcane crushing mill process: A comparative analysis

Model Order Reduction of a Higher Order Model of pH Neutralizer of Sugar Mill