Digitalization, Control and Optimization for Cement Plants

Zhang, Zhanhao; Nielsen, Marcus Krogh; Hørsholt, Steen; Muralidharan, G.; Jørgensen, John Bagterp

doi:10.1016/b978-0-323-88506-5.50203-5

Cited by 3 publications

(4 citation statements)

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“…The methods involve modeling and/or simulation of the electrical energy-intensive cement manufacturing process and applying a statistical method, algorithm, or optimization method to the model. Researchers have explored model predictive control applications [31], intelligent factory technology applications [32], real-time optimization [33], multilinear regression models [35], and reinforcement learning [36]. Energy data can be analyzed to uncover opportunities to reduce EEC.…”

Section: Discussionmentioning

confidence: 99%

“…Several research findings confirm the potential value of digitalization, subsequent data collection, and the application of advanced analytics to the cement manufacturing process. In one instance, implementing model predictive control (MPC) technologies drove power consumption reductions ranging from 3% to 8%, simultaneously enabling increased production output and reduced fuel consumption rates [31]. In this study, two cement-related industrial MPC applications were presented-a cement raw-mix blending application and a cement mill grinding MPC application.…”

Section: Digitalizationmentioning

confidence: 99%

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Towards Leveraging Artificial Intelligence for Sustainable Cement Manufacturing: A Systematic Review of AI Applications in Electrical Energy Consumption Optimization

Oguntola,

Boakye,

Simske

2024

Sustainability

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Cement manufacturing is known for its significant energy consumption and environmental footprint. As the world strives for sustainability, optimizing electrical energy consumption (EEC) in cement manufacturing is essential for reducing operational costs and minimizing the industry’s environmental impact. This systematic review aims to synthesize and analyze existing scholarly works and industry reports on methods and approaches for EEC optimization in cement production. It examines papers published between 1993 and 2023 in academic databases, scholarly journals, and industry publications to identify open questions and areas where future research may be needed. While challenges remain, continued research and innovation are key to further advancements in energy efficiency in cement production. With the advent of Industry 4.0 digitalization and advancements in data analytics and industrial Internet of Things (IIoT), artificial intelligence (AI) can be leveraged to optimize EEC. This study is a review of the applications of artificial intelligence to EEC optimization in industries that have heavy demand for electric power to highlight the value of directing research to its applications in cement manufacturing. The study posits that with digitalization, applying artificial intelligence to extract operational insights from the data collected from embedded sensors and meters at the plant presents the most cost-effective, high-return, and low-risk opportunity to optimize EEC in cement manufacturing.

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Section: Discussionmentioning

confidence: 99%

Section: Digitalizationmentioning

confidence: 99%

Towards Leveraging Artificial Intelligence for Sustainable Cement Manufacturing: A Systematic Review of AI Applications in Electrical Energy Consumption Optimization

Oguntola,

Boakye,

Simske

2024

Sustainability

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“…The total wastes generated and cement manufactured globally per year are 2.01 billion and 4.1 billion tonnes, respectively, as shown in Fig. 2a, which would keep on increasing in the coming years [12][13][14]. Out of these wastes material, 44% of the contribution is from agricultural and food wastes, while remaining 56% contributes from industrial and other wastes, which is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the present review not only focuses on the study of agro-industrial-based wastes as SCM or AAB materials, but also the pozzolanic effects on the properties of binder Fig. 2 a Global cement and waste produced; b classification of wastes produced [12][13][14] Fig. 3 a Industrial waste produced; b agricultural and food wastes produced [15][16][17][18][19][20][21] system when it is calcined and uncalcined.…”

Section: Introductionmentioning

confidence: 99%

Agro-industrial-based wastes as supplementary cementitious or alkali-activated binder material: a comprehensive review

Blesson

Rao

2023

Innov. Infrastruct. Solut.

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An attempt to shed light on the use of agro-industrial-based wastes as supplementary cementitious material (SCM) or as alkali-activated binder (AAB) material is mainly focused in this review paper. The SCM or AAB acts as a suitable replacement for cement, as the cement manufacturing tends to contribute to 8–10% of global CO2 emissions. To gain acceptability in the construction industry, diverse agro-industrial-based wastes must demonstrate their potential as precursors in AAB or as SCM. The binder materials discussed among the agricultural wastes are rice husk ash, olive waste ash, and coconut-based waste ash, and the industrial wastes are ground granulated blast furnace slag, fly ash, and ultra-fine steel slag. The paper also analyses the effects of calcined and uncalcined agro-industrial-based wastes on pozzolanic properties of binder system. It is clear from the studies that the calcined wastes tend to provide greater reactivity compared to the uncalcined wastes, that helps in early strength development in the binder system. The review findings indicate that the agro-industrial-based wastes provide pozzolanic and hydraulic properties, thereby making them suitable as an AAB or as a SCM.

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Bridging the information gap using targeted digital transformation: process mapping large-scale concrete activity

White,

Lees,

MacAskill

2024

Building Research & Information

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Digitalization, Control and Optimization for Cement Plants

Cited by 3 publications

References 1 publication

Towards Leveraging Artificial Intelligence for Sustainable Cement Manufacturing: A Systematic Review of AI Applications in Electrical Energy Consumption Optimization

Towards Leveraging Artificial Intelligence for Sustainable Cement Manufacturing: A Systematic Review of AI Applications in Electrical Energy Consumption Optimization

Agro-industrial-based wastes as supplementary cementitious or alkali-activated binder material: a comprehensive review

Bridging the information gap using targeted digital transformation: process mapping large-scale concrete activity

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