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Deep Learning AI Model

Built from first principles of Engineering and Mathematics (see ellipse diagram), a cutting-edge Neural network is designed for Mondelez International India manufacturing plant to precisely control Weight of chocolate getting deposited in moulds in Closed feedback loop. Architecture below:

Vectors & Matrices First, Second, and Third order Partial Differential Equations Stochastic Probability Distributions Fluid Mechanics Heat Transfer Thermodynamics Chemical Reaction Engineering Electrical & Control Systems GPU Computation
10 sensors 6 sensors 33 sensors 6 parameters
42 sensors 4 sensors 12 sensors 11 sensors 9 parameters
84 parameters
Every 15 mins Multi-Output Deep Neural Network Model Paste Mixer (mixes milk powder, cocoa butter, sugar) Refiner (reduces mix particle size by crushing) Conch (develops chocolate flavors by Mallard Chemical reactions) Quality lab data Auto-correction of Depositor settings Chocolate Storage tanks Chocolate Service tank Temperer (Nucleation & growth of Form-5 Crystals) Depositor (deposits liquid chocolate on plastic moulds) Quality lab data Cooling Tunnel (only chocolate bars sample collection at exit) Quality lab data

From Data to Intelligence

Deep-Learning Neural Network AI systems build to transform your Manufacturing line from Traditional human controlled operation to a Real-time AI controlled operation.

For a given manufacturing line, the AI model is developed from first principles of engineering and applied mathematics, incorporating core process engineering, statistical learning techniques, and advanced neural network architectures. The solution involves mathematical modeling of key process equipment, including kinematic behavior of material flow, mechanical dynamics, and inherent process non-linearities.

The complete solution includes seamless integration with your existing PLC/SCADA infrastructure along with your manual quality lab data, enabling continuous closed-loop feedback automated operation.