Energy and exergy analyses for a cement ball mill of a new generation cement plant and optimizing grinding process: A case study

Ghalandari, Vahab; Iranmanesh, Ahad

doi:10.1016/j.apt.2020.02.013

Cited by 32 publications

(12 citation statements)

References 43 publications

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“…About two-thirds of the total electrical energy is used to reduce the particle size of the raw materials and clinker. Practical methods to optimize the efficiency of the cement ball mill (CBM) were explored in a study of its energy and exergy in a new-generation cement plant [29]. The electrical energy consumption of the CBM unit was initially specified as 37.9 kWh/t.…”

Section: Automation and Process Controlmentioning

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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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: Automation and Process Controlmentioning

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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“…Rather than huge expansion, most industries will focus on maximizing resources for maximum pro tability. Consequently, a study of the impact of operational parameters on ball mill energy e ciency revealed a low particle collision energy will be a re ection of coarse particle size observed at the mill outlet ne products (Fernandes, Halim, &Wahab, 2019;Ghalandari & Iranmanesh, 2020;Simmons, Gorby, &Terembula, 2005). Therefore, to achieve a narrow particle size distribution, applying moderate energy to particles is necessary for this type of operation over a period of use (Tsakalakis & Stamboltzis, 2008).…”

Section: Introductionmentioning

confidence: 99%

Assessment of the thermodynamics efficiency of a cement vertical raw mill using Aspen Plus and artificial intelligence models

Okoji

Anozie

Omoleye

2023

Preprint

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Globally, cement plants are striving to improve their energy efficiency. Therefore, it is critical for cement plant operations to increase the monitoring and control of a vertical raw mill energy process. This technology has attracted the interest of the cement industry with its proven benefits in cement grinding applications. A process simulator was used to study an industrial-scale vertical raw mill (VRM) with 65.4% energy efficiency. The paper proposes further a new model based on grid partitioning, sub-clustering, and fuzzy c-means, which incorporates genetic algorithms (GAs) and particle swarm optimizations (PSOs). VRM data from a steady plant process operation, such as raw material output, material moisture, kiln hot gas, mill fan flow, grinding pressure, and separator speed, was used as input to the prediction model. ANFIS-based prediction models are compared with process simulator predictions to determine the most accurate based on prediction performance criteria. Based on the results, the ANFIS model with sub-clustering assimilated with PSO is the most accurate prediction model for VRM energy efficiency. The coefficient of regression (R2) and root mean square error (RMSE) obtained by this model are 0.945 and 1.3006. The results also showed that VRM's energy efficiency decreased from 65.4 to 64.2% when the separator speed increased from 50 to 75 rpm; product particle size on P90µm decreased from 18.2–10.8%. Finally, the proposed ANFIS based model can be considered to be an efficient technique for predicting the energy efficiency of VRM production processes.

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“…Furthermore, it is only added in small doses, namely 100 -400 ppm, during the final grinding of cement [17]. Although SIA is added in small doses, it can significantly improve cement quality and increase the efficiency of final cement grinding [18]. Another advantage of the addition of SIA is that it can increase cement's fineness, which is closely related to compressive strength.…”

Section: Introductionmentioning

confidence: 99%

Improve the Compressive Strength Using a Strength Improver Agent (Sia) in the Cement Industry in Indonesia

Rahman,

Mulyani

2023

Jurnal Teknologi

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Greenhouse gas emissions such as CO2 are released during clinker production through various processes, including the calcination reaction of limestone (CaCO3). To reduce CO2 emissions per ton of cement produced, some studies have explored the use of clinker substitutes. However, a reduction in the amount of clinker can also result in decreased compressive strength of the cement. To address this issue, the addition of a Strength Improver Agent (SIA) can be used to maintain the necessary compressive strength and ensure compliance with all relevant standards. Therefore, This study aimed to determine the optimal amount of SIA required to achieve the desired compressive strength. The study added SIA in varying amounts (100, 150, 200, 250, 300, 350, and 400 ppm), and the compressive strength of cement was measured at 1, 3, 7, and 28 days based on ASTM C 109 and QPT-LAB-SNI-05 standards. Physical tests were also conducted, including Blaine, 325 mesh residue, Insoluble Residue (IR), Loss on Ignition (LOI), and XRF based on ASTM C 114, ASTM-STP 985, XRF Thermo ARL 8480S. The observations and analysis showed that the optimum amount of SIA addition is 350 ppm, where the resulting compressive strength increases at least 7% compared to blanks.

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Energy and exergy analyses for a cement ball mill of a new generation cement plant and optimizing grinding process: A case study

Cited by 32 publications

References 43 publications

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

Assessment of the thermodynamics efficiency of a cement vertical raw mill using Aspen Plus and artificial intelligence models

Improve the Compressive Strength Using a Strength Improver Agent (Sia) in the Cement Industry in Indonesia

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