Solid Waste Conversion System with NIR Sensor Blending
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Solution Overview
Problem
Converting diverse food waste streams into a blend with a target composition and consistency for human or animal consumption is challenging due to the diversity of waste sources, leading to inefficiencies in disposal and resource utilization.
Innovation Solution
A solid waste conversion system that includes sensors, processing devices, and hoppers to sort, process, and blend food waste, optimizing resource consumption by determining the target composition based on input data and constraints, using near-infrared spectroscopy to analyze nutrient composition and adjust the blending process in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If diverse food waste streams are converted into a blend with target composition, then resource utilization is improved, but the complexity of the conversion process increases
Solution Approach 1:
The system segments diverse food waste streams into distinct categories based on composition (organic vs. inorganic, nutrient content, moisture levels). Sensors analyze each waste stream and direct it to appropriate processing paths, breaking down the complex diversification problem into manageable segments that can be handled systematically.
Solution Approach 2:
The system dynamically adjusts blending parameters (ratios, composition targets, processing conditions) based on real-time sensor data about the incoming waste streams. By changing parameters rather than fixed processes, the system adapts to diverse inputs while maintaining consistent output quality, resolving the contradiction between handling diversity and maintaining process simplicity.
2Manufacturing precision
If sensors and processing devices are used to analyze and sort waste, then manufacturing precision of the blend is improved, but device complexity increases
Solution Approach 1:
The system replaces manual or simple mechanical sorting methods with sensor-based detection (NIR spectroscopy, moisture sensors, weight sensors) that automatically identify waste stream composition. This substitution provides precise analytical capabilities without requiring complex mechanical sorting mechanisms, achieving high manufacturing precision through intelligent sensing rather than mechanical complexity.
Solution Approach 2:
The system uses the waste streams themselves to provide information about their composition through sensor detection. The waste materials effectively 'tell' the system what they are through their interaction with sensors, eliminating the need for extensive external analysis equipment. This self-characterization approach reduces device complexity while maintaining precision.
3Productivity
If real-time blending adjustment is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system implements real-time feedback loops where sensors continuously monitor the blend composition and provide data to controllers that adjust blending ratios accordingly. This feedback mechanism enables automatic real-time optimization of productivity without requiring complex manual intervention or centralized control systems. The feedback principle allows the system to self-regulate and adapt dynamically.
Solution Approach 2:
The blending process is made dynamic rather than static, with blending ratios and processing parameters continuously adjusted based on real-time waste stream characteristics. This dynamic approach allows the system to respond flexibly to changing inputs, maximizing productivity by always operating at optimal conditions rather than fixed parameters.
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
The system effectively reduces pollution and economic waste by converting a significant portion of food waste into a usable blend, optimizing resource consumption and ensuring the blend meets nutritional targets for target consumers.
Implementation Method 1
using near-infrared spectroscopy to analyze nutrient composition and adjust the blending process in real-time
Data Source
AI summary
A method includes causing, by at least one processing device, organic material from at least one hopper of a set of hoppers to be provided to a mixer to create a blend, wherein each hopper of the set of hoppers is to store a respective composition of organic material, obtaining, by the at least one processing device from a sensor, sensor data indicative of an actual composition of the blend, determining, by the at least one processing device based on the sensor data, whether the actual composition of the blend satisfies a threshold condition with respect to a target composition of the blend, and in response to determining that the actual composition of the blend satisfies the threshold condition, causing, by the at least one processing device, the blend to be provided to a drying system for drying.