— A center axis of the classifier is assumed to be the x coordinate and r coordinate in the radial see Fig 1 The powder laden [Air I] flows into the conical shaped classification zone from the top and in the −x direction gravitational direction after dispersed well by the [Air II] The [Air III] flows into the classification zone uniformly through the
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WhatsApp— Then we trained a random forest classifier on the dataset and used LIME to explain individual predictions and visualize model decisions We saw how LIME can be used to identify the most important features for a prediction and how these features can be visualized using a bar chart We also saw how LIME can be used to visualize how the
Lime Hydration is a chemical reaction involving precisely controlled water and quicklime ratio to produce dry hydrated lime powder also called calcium hydroxide The System is called lime hydration p Tel 86 371 5666 5326 Mob 8613838272482 whatsApp /wechat High efficient T Sepax powder classifier is often used Send E mail Get Price
WhatsApp— classifier s operating parameters including the wheel speed and airstream velocity as well as the size and configuration of the classifier wheel may be adjusted to meet given particle size targets Figure 2 Classifier wheel Figure 3 Calculating the cut point d t for a dynamic air classifier Rotation Radial air velocity V r Peripheral
WhatsAppLIME or Local Interpretable Model Agnostic Explanations is an algorithm that can explain the predictions of any classifier or regressor in a faithful way by approximating it locally with an interpretable model It modifies a single data sample by tweaking the feature values and observes the resulting impact on the output It performs the role of an "explainer" to
WhatsApp— Explaining images using LIME image by author Local Interpretable Model agnostic Explanations LIME is one of the most popular Explainable AI XAI methods used for explaining the working of machine learning and deep learning models LIME can provide model agnostic local explanations for solving both regression and
WhatsApp— Similar classifiers were developed by Hosokawa Co [17] for separation of ground coal talc aluminum oxide etc A variant of rotor classifiers Blade Classifier [20] has profiled blades similar to axial compressors Fig 5b The space above the blades is arranged to form a centrifugal crossflow zone rather than the counterflow zone
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WhatsApp— Air classifiers eliminate the blinding and breakage issues associated with screens They work by balancing the physical principles of centrifugal force drag force collision and gravity to generate a high precision method of classifying particles according to size and density For dry materials of 100 mesh and smaller air classification provides
WhatsApp— A center axis of the classifier is assumed to be the x coordinate and r coordinate in the radial see Fig 1 The powder laden [Air I] flows into the conical shaped classification zone from the top and in the −x direction gravitational direction after dispersed well by the [Air II] The [Air III] flows into the classification zone uniformly through the
WhatsApp— First we will build a simple classifier and then implement the explainability The full source code is available in this DataLab workbook LIME explainer can be set up using two main steps 1 import the lime module and 2 fit the explainer using the training data and the targets During this phase the mode is set to classification
WhatsApp— Lime is used to reduce the acid content of milk and to make baking powder The addition of 15% hydrated lime to cement results in a significant reduction in shrinkage and cracking In addition to plaster and stucco lime is used to bind together sand and cement Water and pigment are added to the paint before it is applied to make it a
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WhatsApp— Now many types of powder classifiers are commercially available For example there are cyclone type separators [1] cross flow air type classifiers [2] and impeller wheel type classifiers [3] which are used widely in the manufacturing process of powdered materials although the classifier can classify ultra fine powder in dry conditions
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WhatsApp— Powder Coatings 1/4" Chips 50% < 25 µm Thermo Plastic Polymer 2 5 mm 90% < 1070 µm Mineral Hydrous Kaolin 325 Mesh Hegman Talc 80 Mesh 50% < 7 µm Hydrated Lime 80 Mesh 97% < 25 µm Soft Limestone 1/4" 99% < 25 µm Trona 200 µm 50% < 10 µm Diatomaceous Earth 1 mm 50% < 12 µm Magnesium Oxide 40 Mesh 99% < 325
WhatsApp— LIME aims at finding a linear model which helps to explain classifier decisions for the original instances in X This model is a local approximation of the original classifier function where the simple features in the vector representations x are weighted by their relevances w
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