Currently, the manual method using hand-held infrared temperature measurement instruments for measuring temperatures on the external surfaces of ethylene cracking furnace tubes is highly subjective and is affected by a number of prominent issues, such as the high temperature working environments, which leads to low efficiency and poor measurement accuracy. Hence, an automatic temperature measurement system based on infrared light is designed and realized. In the system, a dual-phase drive synchronization method is proposed to rotate the thermodetector during horizontal movements, thus realizing automatic batch temperature measurements of the furnace tubes. Moreover, a temperature processing algorithm is developed to automatically identify furnace wall and tube surface temperatures, filter out abnormal temperatures and select only high-quality temperature measurements prior to calculating the final result. Real temperature measurement experiments demonstrated that the dual-phase drive temperature measurement system and temperature processing method are effective and efficient. Compared with the traditional manual way, temperatures obtained using the proposed system are more stable and accurate.
The load saturation estimation helps to quantify the final load consumption of a given area, and avoid unnecessary investment to the transmission and distribution facilities. There are generally two methods to estimate the saturated load, based on the load curve and the spacial load distribution respectively. With the historical load instead of load classification data, the Logistic curve, i.e. the S curve, is more suitable to extrapolate the load curve, and forecast the saturated load consumption.In the existing literatures, the parametric estimation of the Logistic curve is based on randomly selected 3 or 4 load data with equal intervals, and can not avoid abnormal or ill data. In this paper, improved parametric estimation methods are proposed. With the average value or the largest correlation index is applied to find the parameters of Logistic curve. The numerical results among the proposed and existing methods are presented, and the forecast feasibility for different load increase stages are discussed.
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