Gas Purification Control Using Learning Models for Waste Syngas
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Solution Overview
Problem
Combustible waste is difficult to reuse as industrial raw materials due to its heterogeneous nature and wide fluctuations in composition, limiting its efficient recycling.
Innovation Solution
A control device and method that utilize a learning model trained on gas information, control information, and feature information from a gas purification process to optimize the operation of a gas purification device, ensuring efficient reuse of combustible waste as industrial raw materials by effectively purifying the gas generated from waste conversion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If combustible waste is directly reused as industrial raw materials, then resource efficiency improves, but the heterogeneous nature and wide fluctuations in composition make it difficult to achieve consistent quality
Solution Approach 1:
The patent transforms the physical and chemical parameters of combustible waste through gasification, converting it into synthesis gas with controlled composition. This parameter transformation enables the heterogeneous waste to be converted into a more uniform gas product that can serve as reliable industrial raw material, resolving the contradiction between resource efficiency and quality consistency.
Solution Approach 2:
The patent introduces a gas purification device as an intermediary between the gasifying furnace and the final product. This intermediary component removes impurities from the synthesis gas, ensuring that the fluctuations in waste composition do not directly affect the final product quality, thus maintaining both high resource efficiency and consistent quality.
2Ease of manufacture
If traditional gas purification methods are used, then the purification process is simple, but the purification effectiveness is insufficient to handle fluctuating waste compositions
Solution Approach 1:
The patent implements a feedback control system where the learning model continuously monitors the composition of the gas and adjusts the purification device operations accordingly. This feedback mechanism enables the system to adapt to fluctuating waste compositions while maintaining effective purification, resolving the contradiction between process simplicity and purification effectiveness.
Solution Approach 2:
The learning model enables the purification system to automatically adjust its own operations based on real-time data without requiring complex manual intervention. This self-service capability maintains purification effectiveness while keeping the overall system relatively simple, as the automation handles the complexity of adapting to varying waste compositions.
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
Enables the high-efficiency reuse of combustible waste as industrial raw materials by optimizing the gas purification process, even with fluctuating waste compositions, thereby enhancing the production of valuable chemicals like ethanol.
Implementation Method 1
a gasifying furnace for converting collected waste to gas
Implementation Method 2
an adsorbent which is capable of adsorbing impurities contained in the synthesis gas
Data Source
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AI summary
The object is to provide a control device, an operation control device, a server, a management server, a computer program, a learning model, a control method and an operation control method that enable reuse of combustible waste as industrial raw materials with high efficiency. A control device comprises a gas information acquisition unit that acquires gas information on gas converted by a gasifying furnace for converting collected waste to gas; a control information acquisition unit that acquires control information controlling the gas purification device for purifying gas converted by the gasifying furnace; a feature information acquisition unit that acquires feature information including information on purified gas purified by the gas purification device; and a creation unit that creates a learning model by machine learning based on the gas information, the control information and the feature information.