Active disturbance rejection-based industrial cascade control plant with cloud monitoring

Bhaskarwar, Tushar; Aole, Sumit; Chile, R. H.

doi:10.1108/jedt-12-2020-0536

Cited by 3 publications

(2 citation statements)

References 33 publications

(31 reference statements)

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“…The business establishments are getting more and more benefitted by BDA for improving their competitiveness (Zhang et al , 2020). Insights imparted by BDA are instrumental in enabling real-time measurement, monitoring of business processes and improving the quality management (Waller and Fawcett, 2013; Bhaskarwar and Aole, 2021). A business decision is inevitably based on the available data.…”

Section: Literature Surveymentioning

confidence: 99%

Identification of critical factors for big data analytics implementation in sustainable supply chain in emerging economies

Jain¹,

Tambuskar²,

Narwane

2022

JEDT

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Purpose The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose. Design/methodology/approach Through a comprehensive literature review and experts’ opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis. Findings The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain. Research limitations/implications The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust. Practical implications The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan. Social implications The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society. Originality/value This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.

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Section: Literature Surveymentioning

confidence: 99%

Identification of critical factors for big data analytics implementation in sustainable supply chain in emerging economies

Jain¹,

Tambuskar²,

Narwane

2022

JEDT

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“…However, they are tied together by either proprietary-based technology or standardisation, allowing data along with an application to be ported across several platforms (Andreadis et al , 2015). The available cloud services are categorised as “Platform as a Service” (PaaS), “Infrastructure as a Service” (IaaS) and “Software as a Service” (SaaS) (Bhaskarwar et al , 2021; Creeger, 2009; Narkhede et al , 2020; Narwane et al , 2020; Naseri and Navimipour, 2019; Souri et al , 2018). SaaS is a cloud service where the consumer can usually access software applications on the internet, which can be used for a wide range of tasks and are extensively hosted on the cloud that can be done for both organisations and individuals.…”

Section: Introductionmentioning

confidence: 99%

Role of cloud computing technology in the education sector

Thavi

Jhaveri

Narwane

et al. 2021

JEDT

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Purpose This paper aims to provide a literature review on the cloud-based platforms for the education sectors. The several aspects of cloud computing adoption in education, remote/distance learning and the application of cloud-based design and manufacturing (CBDM) have been studied and theorised. Design/methodology/approach A four-step methodology was adopted to analyse and categorise the papers obtained through various search engines. Out of 429 research articles, 72 papers were shortlisted for the detailed analysis. Findings Many factors that influence cloud computing technology adoption in the education sector have been identified in this paper. The research findings on several research items have been tabulated and discussed. Based on the theoretical research done on cloud computing for education, cloud computing for remote/distance learning and CBDM, cloud computing could enhance the educational systems in mainly developing countries and improve the scope for remote/distance learning. Research limitations/implications This study is limited to papers published only in the past decade from 2011 to 2020. Besides, this review was unable to include journal articles published in different languages. Nevertheless, for the effective teaching and learning process, this paper could help understand the importance and improve the process of adopting cloud computing concepts in educational universities and platforms. Originality/value This study is a novel one as a research review constituting cloud computing applications in education and extended for remote/distance learning and CBDM, which have not been studied in the existing knowledge base.

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Designing multivariable PI controller with multi-response optimization for a pilot plant binary distillation column: a robust design approach

Bhat

Thirunavukkarasu

Selvanathan

et al. 2023

JEDT

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Purpose The purpose of this paper is to propose and validate a robust industrial control system. The aim is to design a Multivariable Proportional Integral controller that accommodates multiple responses while considering the process's control and noise parameters. In addition, this paper intended to develop a multidisciplinary approach by combining computational science, control engineering and statistical methodologies to ensure a resilient process with the best use of available resources. Design/methodology/approach Taguchi's robust design methodology and multi-response optimisation approaches are adopted to meet the research aims. Two-Input-Two-Output transfer function model of the distillation column system is investigated. In designing the control system, the Steady State Gain Matrix and process factors such as time constant (t) and time delay (?) are also used. The unique methodology is implemented and validated using the pilot plant's distillation column. To determine the robustness of the proposed control system, a simulation study, statistical analysis and real-time experimentation are conducted. In addition, the outcomes are compared to different control algorithms. Findings Research indicates that integral control parameters (Ki) affect outputs substantially more than proportional control parameters (Kp). The results of this paper show that control and noise parameters must be considered to make the control system robust. In addition, Taguchi's approach, in conjunction with multi-response optimisation, ensures robust controller design with optimal use of resources. Eventually, this research shows that the best outcomes for all the performance indices are achieved when Kp11 = 1.6859, Kp12 = −2.061, Kp21 = 3.1846, Kp22 = −1.2176, Ki11 = 1.0628, Ki12 = −1.2989, Ki21 = 2.454 and Ki22 = −0.7676. Originality/value This paper provides a step-by-step strategy for designing and validating a multi-response control system that accommodates controllable and uncontrollable parameters (noise parameters). The methodology can be used in any industrial Multi-Input-Multi-Output system to ensure process robustness. In addition, this paper proposes a multidisciplinary approach to industrial controller design that academics and industry can refine and improve.

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Active disturbance rejection-based industrial cascade control plant with cloud monitoring

Cited by 3 publications

References 33 publications

Identification of critical factors for big data analytics implementation in sustainable supply chain in emerging economies

Identification of critical factors for big data analytics implementation in sustainable supply chain in emerging economies

Role of cloud computing technology in the education sector

Designing multivariable PI controller with multi-response optimization for a pilot plant binary distillation column: a robust design approach

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