Lg Chem, Ltd

Yeon-Uk Choo

Jeong-Ju Cho

Ho-Chun Lee

Tae-Woo Kim

Joon-Ho Shin

Mi-Young Son

Sung-Kyu Lee

Hyun-Kon Song

Sung-Kyun Kim

Ha-Na Lee

Sa-Eun Park

Disclosed is a method for purifying phenol, by which the loss of a heat source is minimized, the amount used of steam is decreased, and efficiency of energy consumed in a process may be maximized. The method for purifying phenol includes a step of passing impurities including phenol, acetone and hydrocarbon, which are prepared using cumene as a raw material, via a plurality of distillation columns one by one, and performing azeotropic distillation, wherein the ratio of phenol and water at the uppermost end of the distillation column where the azeotropic distillation is performed, is from 60:40 to 65:35, for the optimized separation of the hydrocarbon.

Method for purifying phenol

Provided is a method for preparing ethylene with improved energy efficiency which can reduce energy due to direct heat exchange between processes.

Method for preparing ethylene with improved energy efficiency

A system for estimating long term characteristics of a battery includes a learning data input unit for receiving initial characteristic learning data and long term characteristic learning data of a battery to be a learning object; a measurement data input unit for receiving initial characteristic measurement data of a battery to be an object for estimation of long term characteristics; and an artificial neural network operation unit for receiving the initial characteristic learning data and the long term characteristic learning data from the learning data input unit to allow learning of an artificial neural network, receiving the initial characteristic measurement data from the measurement data input unit and applying the learned artificial neural network thereto, and thus calculating long term characteristic estimation data from the initial characteristic measurement data of the battery and outputting the long term characteristic estimation data.

System and method for estimating long term characteristics of battery

A system includes a learning data input unit for receiving initial and long term characteristic learning data of a battery to be a learning object; a measurement data input unit for receiving initial characteristic measurement data of a battery to be an object for long term characteristic estimation; an artificial neural network operation unit for converting the learning data into first and second data structures, allowing an artificial neural network to learn the learning data based on each data structure, converting the measurement data into first and second data structures, and individually applying the learned artificial neural network corresponding to each data structure to calculate and output long term characteristic estimation data based on each data structure; and a long term characteristic evaluation unit for calculating an error of the estimation data of each data structure and determining reliability of the estimation data depending on error.

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Daejeon, South Korea

Yeon-Uk Choo