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Feed-grindability variations (clinker hardness) in cement grinding circuit affects the product quality and productivity of the cement plant. This investigation proposes a generalized predictive controller for cement grinding circuit that is more robust to feed-grindability variations. The main building block of the proposed controller is a new model for cement grinding circuit that directly relates product quality with elevator current and main drive load. The advantage of the model is that the effect of feed-grindability variations on product quality can be easily observed from the output. To develop such a model, this investigation adapts a data driven modelling approach. Experimental data obtained as measurements from cement grinding circuit in a cement mill located near Chennai, India is used to develop the transfer function model based on least squares approach. The model obtained from data driven modelling is used to design a generalized predictive controller whose objective is to optimize the product quality in the presence of feed-grindability variations without breaching physical and operational limits of the cement grinding circuit. The tuning parameters of the proposed generalized predictive controller is adjusted to meet performance metrics specific to cement industries. Our results show that the proposed controller provides better product quality in the presence of feed-grindability variations than other optimization based controllers such as the linear quadratic regulator.

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An uncomplicated and rapid procedure has been developed for the quantitative analysis of sucrose in fruit samples (grape, pineapple, mango) through attenuated total reflectance-Fourier transform infrared absorbance measurements (ATR-FTIR). FTIR analysis takes considerably reduced time compared to the other classical methods. To calibrate the method, we used firstly, different concentrations of pure sucrose (from 1 to 5 %) and registered their IR maximal wavenumbers and peak intensity. The spectral peak of sucrose for each sample lies between 1057 and 1061 cm -1 . DNS method was used to analyse the content of sucrose by using spectrophotometry. The wave length used for analysing is 540 nm. Also high performance liquid chromatography was used to analyse the sucrose content in the fruit juices. By comparing the retention time of sucrose standards and the sample juices, sucrose concentration was identified and quantified. The results of all three experiments/techniques support each other by justifying that the mango has the high content of sucrose followed by pineapple and grape.