CAJ | 학술논문

目前由于部分电站锅炉所用到的燃煤大多为配煤，在有些情况下，其复杂的物理化学特性导致难以获得高精确度的常规近红外定量分析模型，这给电煤发热量的在线检测带来了一定困难。针对该问题，深入研究了电煤近红外光谱时域和频域特征，提出一种能够通过全局优化策略自动在频域内建立最优近红外定量分析模型的新方法-频域自适应分析法。该方法首先将时域近红外光谱通过快速傅里叶变换转换为频域近红外信号；然后采用有效光谱能量率得到合适的频域信息范围；接着根据近红外光谱频域下的相关系数谱图、方差谱图以及谐波在频域中的坐标合理构建了频域信息量评价参数，利用该参数对模型输入变量的种群位置进行初始化；最后采用频域分区搜索和综合性能评价函数得到最佳建模方案。与此同时，结合电煤煤粉近红外图谱的特性，并以其发热量为待测目标对该方法进行了验证，取得相对较好实验效果，与传统方法主成分回归、偏最小二乘回归、反向传播神经网络以及基于遗传算法的偏最小二乘回归和支持向量机回归相比，该方法预测精度更高，并且有效避免了频域随机搜索潜在的过拟合和虚假有效模型的弊端，具有良好的应用前景。此外，该方法也可推广用于其他类型的光谱定量分析。
목전유우부분전참과로소용도적연매대다위배매，재유사정황하，기복잡적물이화학특성도치난이획득고정학도적상규근홍외정량분석모형，저급전매발열량적재선검측대래료일정곤난。침대해문제，심입연구료전매근홍외광보시역화빈역특정，제출일충능구통과전국우화책략자동재빈역내건립최우근홍외정량분석모형적신방법-빈역자괄응분석법。해방법수선장시역근홍외광보통과쾌속부리협변환전환위빈역근홍외신호；연후채용유효광보능량솔득도합괄적빈역신식범위；접착근거근홍외광보빈역하적상관계수보도、방차보도이급해파재빈역중적좌표합리구건료빈역신식량평개삼수，이용해삼수대모형수입변량적충군위치진행초시화；최후채용빈역분구수색화종합성능평개함수득도최가건모방안。여차동시，결합전매매분근홍외도보적특성，병이기발열량위대측목표대해방법진행료험증，취득상대교호실험효과，여전통방법주성분회귀、편최소이승회귀、반향전파신경망락이급기우유전산법적편최소이승회귀화지지향량궤회귀상비，해방법예측정도경고，병차유효피면료빈역수궤수색잠재적과의합화허가유효모형적폐단，구유량호적응용전경。차외，해방법야가추엄용우기타류형적광보정량분석。
At present ,because the blending coal was taken in some power stations as the major fuel which has too complex physical and chemical characters to build accurate normal near infrared quantitative models in some cases ,which brought difficulties for on-line electric coal calorific value detection .For this reason ,it was care-fully studied that the time domain and frequency domain properties of the power generation coal near infrared spectra ,and was proposed that a new quantitative near infrared method named frequency domain self-adaption analysis .The first step ,time domain near infrared spectra are converted into frequency domain near infrared signal by Fast Fourier Transform ;The second step ,the suitable frequency information range by means of valid spectra energy parameter ηE was obtained by this method ;The third step ,it was constructed that an informa-tion volume parameter which is formed by correlation coefficient ,standard deviation spectra and coordinate of harmonic in frequency domain to initialize the regression model input parameters ’ position;Finally ,the opti-mal model is established by way of discrete frequency domain scooping and synthesized performance function . At the same time ,compared with the principle component regression ,partial least squares regression ,back propagation artificial network ,support vector regression and partial least squares regression optimized by ge-netic algorithm models ,it is acquired that a more accurate method which can effectively avoid over fitting and virtual effective models and has a very useful application prospect by verifying the electric coal calorific value . Additionally ,this method can be used in other quantitative spectra analysis .