The minimum air pressure necessary at the grate to penetrate a bed of heated clinker of a certain thickness and permeability is known as under‐grate pressure. It is vital to keep the under‐grate pressure consistent because excessive pressure causes the clinker to form an unstable suspension and minimum pressure allows heat from the clinker to harm the cooler grates. So it is necessary to obtain excellent performance through reasonable control methods. For this purpose, this article proposes an optimization approach for a proportional integral derivative (PID) controller to control the pressure below the grate of the clinker cooler. The proposed optimization approach is the honey badger algorithm (HBA), which mimics the feeding habits of honey badgers. The main aim of proposed approach is to maintain and regulate under‐grate pressure of clinker cooler using HBA and provide optimum cooling rate to clinker. The proposed approach based grate cooler also provide the less electricity consumption and stable temperature. The proposed technique is implemented in MATLAB and its efficiency is compared to various existing techniques. From the simulation, the electricity consumption of proposed approach RMSE is 158 and mean accuracy is 98.65. Under‐grate pressure based RMSE is 5.01 and the mean accuracy become 99.55. The outlet temperature based RMSE of proposed approach is 4.32 and mean accuracy is 81.21. These values are lower than existing approaches, it reveals the proposed approach is better than the existing one.
In this article, the modified local binary patterns based feature extraction and hyper parameters tuned attention segmental recurrent neural network classifier with flamingo search optimization algorithm (MLBPFE-ASRNNFSOAC-DDM) is proposed for the disease diagnosis model. Here, breast cancer, diabetic, chronic kidney diseases diagnosis model is implemented. Initially, images are considered as dataset for disease diagnosis, which is given to the altered phase preserving dynamic range compression (APPDRC) scheme for preprocessing process. This APPDRC is used to preserve local features for boundary detection, thus; recovers image quality. Then the morphological, grayscale statistical and Haralick texture features are taken from preprocessed image with the help of modified local binary pattern process. The extracted features are given with attention segmented recurrent neural network (ASRNN) for classification. Then the weight parameters of ASRNN classifier are optimized by flamingo search optimization algorithm (FSOA), which increases the classification accuracy. This simulation process is accomplished at MATLAB platform. The proposed method, during Diabetic Diagnosis Model attains higher accuracy 16.4%, 21.45%, 30.38%, and 21.01% compared with the existing methods like FE-ResNetV2DNNMSOC-DDM and CMVHHO-DKMLC-DDM, CNN-CAD-DDM, and AD-CFDRI-SDL, respectively. During breast cancer Diagnosis Model attains higher accuracy 27.3%, 20.56%, 31.34%, and 25.13% compared with the existing methods like CMVHHO-DKMLC-BCM and SVMFE-OPFPSOC-BCM, MLPNN-CNN-BCM, and DCNN-EL-BCM, respectively.
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