Concrete Mix Optimization via Machine Learning Control
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Solution Overview
Problem
The concrete industry faces challenges in consistently producing high-quality concrete due to variability in water content and environmental factors, relying heavily on the skill of batchmen and lacking a regimented system for monitoring and adjusting concrete mixes in real-time.
Innovation Solution
A centralized control system that assigns unique serial numbers to concrete batches, collects and stores data on cement, water, sand, and aggregate quantities, and utilizes machine learning to optimize ingredients and attributes based on performance criteria, reducing manual adjustments and variability in concrete production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual mixing control is used, then operational flexibility is maintained, but manufacturing precision and consistency deteriorate
Solution Approach 1:
The patent replaces manual mechanical control systems with an automated computer-based control system that uses sensors, processors, and automated mixing equipment to precisely control concrete mix proportions and timing, thereby improving manufacturing precision while reducing reliance on operator skill variability
Solution Approach 2:
The patent implements feedback control by using sensors to monitor actual mix conditions (such as water content, temperature, and consistency) and automatically adjusting mixing parameters in real-time based on measured deviations from target specifications, ensuring consistent manufacturing precision
2Manufacturing precision
If real-time monitoring is implemented, then manufacturing precision improves, but device complexity increases
Solution Approach 1:
The patent uses sensors to continuously monitor concrete mix properties (water content, temperature, consistency) and feeds this information back to the control system, which automatically adjusts mixing parameters in real-time, achieving high manufacturing precision through automated feedback control
Solution Approach 2:
The control system automatically monitors and adjusts mixing parameters without requiring manual intervention, allowing the system to self-regulate and maintain precise batch specifications through automated decision-making based on sensor data
3Manufacturing precision
If centralized control is implemented, then manufacturing precision improves, but loss of time increases
Solution Approach 1:
The patent pre-establishes target specifications, tolerances, and control parameters before the mixing process begins, allowing the automated system to operate within predetermined guidelines and reducing real-time decision-making delays while maintaining high manufacturing precision
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This system reduces raw material costs, carbon footprint, and human errors, while improving consistency and reducing delivery time by making accurate, automated batch adjustments, leading to fewer concrete problems associated with variable performance.
Implementation Method 1
The pozzolanic chemical reaction is that which causes concrete to get hard. The pozzolanic reaction requires water and is an exothermic reaction giving off heat as the cement in the mix hydrates.
Implementation Method 2
The pozzolanic reaction requires water and is an exothermic reaction giving off heat as the cement in the mix hydrates.
Implementation Method 3
The pozzolanic reaction requires water and is an exothermic reaction giving off heat as the cement in the mix hydrates.
Data Source
AI summary
A method is disclosed for mixing and for placing a batch of concrete mix in forms includes assigning a unique serial number to the batch of concrete stored in a database. Admitting measured quantities of concrete ingredients into a mixing vessel, the ingredients including a cement quantity, a water quantity a sand quantity and an aggregate quantity forms the batch. A network collects and stores each of the cement quantity, the water quantity, the sand quantity, and the aggregate quantity in association with the serial number. After curing the batch of concrete mix, the cured batch of concrete mix is tested to derive at least one performance criterion. The performance criterion is stored in the network in association with the serial number. Machine learning is exploited to optimize the ingredients and attributes of the batch based upon the performance criterion.