CAJ | 학술논문

间歇过程变量的在线预测是一种重要的生产过程质量控制手段。实现间歇过程变量的在线预报需要对过程以往的批次数据建立预测模型，即需挖掘批次间和批次内的数据信息。针对间歇过程数据不同批次不等长、数据长度短、非线性等特点，采用数据重构——自回归求和滑动平均方法建立其在线预测模型：将收集到的间歇过程变量以批次为单位进行数据平滑；对这些批次数据按照随机的顺序首尾相接，组成长数据集；对于批次连接处数据跳跃的情况，采用后面所有批次数据减去上一批次的最后一个值，以实现数据的平滑；采用自回归求和滑动平均方法建立数据模型，并用于间歇蒸馏温度的在线预报。采用该方法建立的4步预测模型对某间歇蒸馏过程上升气温度的预测均方差较小，符合生产现场的预测要求。
간헐과정변량적재선예측시일충중요적생산과정질량공제수단。실현간헐과정변량적재선예보수요대과정이왕적비차수거건립예측모형，즉수알굴비차간화비차내적수거신식。침대간헐과정수거불동비차불등장、수거장도단、비선성등특점，채용수거중구——자회귀구화활동평균방법건립기재선예측모형：장수집도적간헐과정변량이비차위단위진행수거평활；대저사비차수거안조수궤적순서수미상접，조성장수거집；대우비차련접처수거도약적정황，채용후면소유비차수거감거상일비차적최후일개치，이실현수거적평활；채용자회귀구화활동평균방법건립수거모형，병용우간헐증류온도적재선예보。채용해방법건립적4보예측모형대모간헐증류과정상승기온도적예측균방차교소，부합생산현장적예측요구。
Online prediction of batch process variables is an important quality control means for production process. The production data with previous batches are in need to develop the prediction model for realizing online prediction of process variables, that is to mine the relationship among different batches and within the batches. A nonlinear online prediction model based on data reconstruction-autoregressive integrated moving average method is proposed according to the batch process data characters of nonlinearity, short data length and unequal data lengths of different batches. The collected batch process data are smoothed as batch unit. All these batch data are linked to each other randomly from the very beginning to the end, and form a long data collection. The uncontinuity of two continuous hatches is processed by subtracting the last data of previous batch to realize the data smooth. A data model is developed using autoregressive integrated moving average method, and used for the online prediction of batch distillation temperature. The mean square error with the developed four step prediction model for the updraft temperature of one batch distillation process is proved to be precise and exact, and consistent to the prediction requirement of the site.