Predictive Model Optimizing Lignocellulosic Refining Energy
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Solution Overview
Problem
The Thermomechanical Pulping Process (TMP) experiences high energy consumption and variability in pulp quality due to variations in wood chip properties, leading to inefficiencies in energy use and pulp production, with existing control strategies not adequately accounting for chip quality as an external disturbance.
Innovation Solution
A method and system utilizing a predictive model and optimizer to control the lignocellulosic granular matter refining process, which includes online measurement of chip properties and refining process parameters to adapt and optimize energy consumption and pulp quality, by generating optimal control targets based on predicted values and process constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional feedback control strategies are used to manage refining process, then process stability is maintained, but energy consumption remains high and pulp quality variability increases due to inability to account for chip quality variations
Solution Approach 1:
The system performs preliminary measurement of chip properties (density, moisture, size) before the refining process and uses this information to predict required refining energy and optimize process parameters in advance, preventing energy waste and quality variations rather than reacting to them after occurrence
Solution Approach 2:
The system implements a closed-loop control strategy where pulp quality parameters (CSF, fibre length, fines content) are measured and fed back to the optimization system, which continuously adjusts refining parameters to maintain quality consistency while minimizing energy consumption despite variations in chip properties
2Manufacturing precision
If refiners operate at high energy input to ensure pulp quality meets specifications, then pulp quality is improved, but energy consumption increases significantly
Solution Approach 1:
The system dynamically adjusts refiner operating parameters (motor load, dilution flow rate, plate gap) based on real-time chip property measurements and predicted pulp quality requirements, allowing the process to adapt its energy input to the actual needs of each chip batch rather than operating at fixed high energy levels
Solution Approach 2:
The optimization system changes multiple process parameters simultaneously (refining energy, dilution flow rate, screw feeder speed) based on chip quality variations to achieve the desired pulp quality with minimal energy consumption, rather than relying on a single parameter adjustment
3Productivity
If chip quality variations are not accounted for in control strategy, then control system simplicity is maintained, but process efficiency decreases and pulp quality variability increases
Solution Approach 1:
The system introduces a predictive model as an intermediary between chip property measurements and refiner control, which calculates optimal process parameters and predicted pulp quality without requiring complex direct control algorithms, thus improving efficiency while managing complexity through modular architecture
Data Source
AI summary
A system and method for optimizing a process for refining lignocellulosic granular matter such as wood chips use a predictive model including a simulation model based on relations involving a plurality of matter properties characterizing the matter such as moisture content, density, light reflection or granular matter size, refining process operating parameters such as transfer screw speed, dilution flow, hydraulic pressure, plate gaps, or retention delays, at least one output controlled to a target such as primary motor load or pulp freeness, and at least one uncontrolled output such as specific energy consumption, energy split, long fibers, fines and shives. An adaptor is fed with measured values of matter properties and measured values of controlled and uncontrolled outputs, to adapt the simulation model accordingly. An optimizer generates a value of the target according to a predetermined condition on a predicted uncontrolled output parameter and to one or more process constraints.