生物质厌氧发酵-气化耦合过程仿真与反向设计平台数据集

针对耦合技术过程物质流与能量流评估模型，总结耦合技术全过程的工艺参数，分析各工艺参数间的内在联系与数学关系，通过机器学习算法在保留参数特征性的前提下进行数据维度压缩；建立降维数据与耦合系统末端能量流、物质流、环境流控制指标之间的前馈人工神经网络模型。采用不同的隐藏层与神经元结构对模型预测效果进行优化调试。基于耦合过程元素迁移转化路径网络分析该工艺过程中元素间内在的迁移联系，确定相关指标参数，形成系统动态评估算法。通过模拟调试的方法分析动态评估算法对耦合系统控制参数的敏感性特征，确定耦合工艺过程的关键控制信号。开发耦合系统关键工艺信号的采集与数值传输方法，构建输入信号与动态评估系统之间的动态反馈体系，并对响应反馈的实时性与准确性进行反向优化设计。

Focusing on the material and energy flow assessment model for coupled technological processes, this study summarizes the process parameters across the entire coupled technology workflow, and analyzes the internal correlations and mathematical relationships among these parameters. Data dimensionality compression is performed via machine learning algorithms while preserving the characteristic features of the parameters. A feedforward artificial neural network model is established between the dimension-reduced data and the control indicators of energy flow, material flow and environmental flow at the terminal of the coupled system. The predictive performance of the model is optimized and tuned by adopting different hidden layer and neuron structures. Based on the element migration and transformation path network of the coupling process, the internal migration connections among elements in the technological process are analyzed, relevant indicator parameters are determined, and a systematic dynamic assessment algorithm is developed. The sensitivity characteristics of the dynamic assessment algorithm to the control parameters of the coupled system are analyzed through simulation and tuning, and the key control signals for the coupled technological process are identified. Methods for collecting and numerically transmitting the key process signals of the coupled system are developed, a dynamic feedback system between the input signals and the dynamic assessment system is constructed, and reverse optimization design is carried out for the real-time performance and accuracy of the response feedback.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于生物质厌氧发酵-气化耦合过程的仿真与反向设计，通过建立物质流和能量流评估模型，并利用机器学习算法进行数据压缩和神经网络建模。它旨在分析工艺参数间的内在联系，开发动态评估算法，并优化关键控制信号的反馈系统。