Neural Fuel Prediction for Cement Preheater Temperature Control

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Solution Overview

Problem

In cement manufacturing, maintaining the appropriate process temperature in preheating chambers is challenging due to the non-uniform composition of recycled fuels, which affects their calorific value and makes it difficult to control fuel input efficiently.

Innovation Solution

An electronic device equipped with trained neural network models predicts the calorific value of recycled fuels and the temperature of preheating chambers, providing guidance to maintain optimal process temperatures by adjusting fuel input accordingly.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Object-affected harmful factors

If recycled fuel is used to reduce carbon emissions, then environmental performance is improved, but the non-uniform composition and varying calorific value make temperature control difficult

Engineering Contradiction:

Improvecarbon emissionsVSAvoidpreheating chamber temperature control

Core Design Contradiction:

Object-affected harmful factorsVSTemperature

Solution Approach 1:

The system performs preliminary prediction of the calorific value of recycled fuel using a neural network model before the fuel is actually burned. This advance prediction allows the control system to pre-adjust the fuel input amount to compensate for variations in calorific value, thereby maintaining stable preheating chamber temperature despite using recycled fuel with non-uniform composition

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements a closed-loop feedback control mechanism where the predicted calorific value is fed back to adjust the fuel input amount. The neural network continuously predicts calorific value based on fuel characteristics, and this prediction is used to dynamically adjust the fuel input to maintain the preheating chamber temperature within the desired range, resolving the contradiction between using recycled fuel and maintaining temperature control

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If the calorific value of recycled fuel is not uniformly identified, then fuel flexibility is improved, but temperature prediction accuracy deteriorates

Engineering Contradiction:

Improvefuel flexibilityVSAvoidtemperature prediction accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system replaces traditional physical measurement methods for determining calorific value with an intelligent neural network-based prediction system. The neural network analyzes fuel composition data and predicts calorific value without requiring complex physical testing, thereby maintaining fuel flexibility while achieving accurate temperature prediction through computational intelligence rather than mechanical measurement

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the approach from directly measuring physical fuel properties to using computational parameters (neural network predictions) to estimate calorific value. By transforming the problem from physical measurement to computational prediction, the system maintains adaptability to various fuel types while achieving the precision needed for accurate temperature prediction

Inventive Principle:

Principle #35Parameter changes

3Device complexity

If traditional fuel input control methods are used, then system simplicity is maintained, but energy efficiency and temperature control performance deteriorate

Engineering Contradiction:

Improvecontrol system complexityVSAvoidenergy efficiency

Core Design Contradiction:

Device complexityVSLoss of energy

Solution Approach 1:

The control system performs self-optimization by using the neural network to automatically predict calorific value and determine the optimal fuel input amount without requiring complex external control mechanisms. The system serves itself by integrating the prediction and control functions within a unified framework, improving energy efficiency while keeping the overall system architecture relatively simple

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250036090A1Electronic device for implementing system for predicting and controlling industrial processes, and control method thereof

Publication Date: 2025.01.30 INEEJI CO LTD

AI summary

Disclosed is an electronic device for implementing an industrial process prediction and control system. The electronic device includes one or more processors configured to perform predicting on a calorific value of recycled fuel and a temperature of a preheating chamber in a cement manufacturing apparatus using the trained first neural network model and the trained second neural network model based on the process information including fuel input information of a cement manufacturing apparatus, and controlling input fuel for cement based on this prediction.