Download Citation | On Sep 1, 2023, Mohd Zaki and others published Cementron: Machine learning the alite and belite phases in cement clinker from optical images | Find, read and cite all the ...
view moreFree lime content is an important quality parameter in the production of clinker, targeted between 0.5% to 1.5%. Existing studies have tried to predict absolute free lime content, using soft sensors data, with limited success due to the complexity of the clinker burning process. Subject matter experts believe that, instead of predicting the free lime absolute …
view more2023713 · Here, we present a machine learning (ML) approach to automatically and accurately detect the major clinker microstructure phases, namely, alite and belite, from optical microscopy images. To this extent, we create an annotated dataset of cement clinker by manually labelling alite and belite phases in microscopy images.
view moreIn comparison, the novel CO2-activated self-pulverising cement described in this paper promises similar reductions in manufacturing energy and CO2 emissions to those of Solidia cement, but with the added advantage of dispensing with the need for clinker grinding.
view moreFor the heat balance and thermal efficiency calculation of the cement rotary kiln firing system, the heat of cement clinker formation plays an important role in energy consumption. In the detailed energy consumption analysis of the cement firing system, the heat of clinker formation has different energy distributions in different production processes of the …
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view more2020101 · Cement and Concrete Research (29) 531-535. This research focused the role to study the effects and ways to control the chemical composition of clinker for better cement production. Cement is a substance produced by grinding a mixture of a clay and limestone and heating to a temperature of 1450°C, in which the chemical transformation …
view more2022118 · uniformity of the images, variations in the geometry and size of the phases, and variabilities in the experimental approaches and imaging methods make it challenging to obtain the phases. Here, we present a machine learning (ML) approach to detect clinker microstructure phases
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