The Merck Index of Chemicals and Drugs An Encyclopedia for the Chemist, Pharmacist, Physician and Allied Professions Sixth edition Pp xiv + 1167 (Rahway, NJ: Merck and Company, Inc, 1952) 750 dollars; thumb-indexed, 8 dollars

The Merck Index

Modeling of methane adsorption capacity in shale gas formations using white-box supervised machine learning techniques

The accurate prediction of the state of health for retired batteries is the premise to ensure the safe and efficient operation of echelon utilization batteries. Aiming at the problems of limited battery cycle data and coupling of health status parameters, an assessment method of the state of health for echelon utilization batteries based on deep neural network learning with error correction is proposed in this paper. According to the reference discharge curve of echelon utilization batteries, the main characteristic parameters characterizing the performance aging of echelon utilization batteries are mined, and the state of health evaluation model of echelon utilization batteries based on deep neural network learning is established after the dimensionality of these characteristic parameters are reduced by the grey correlation analysis method. Markov chain error correction is used to further improve the prediction accuracy of the established deep neural network model. The effectiveness of the proposed method is verified by the simulation analysis of lithium-ion battery cycle test data from NASA Ames Prediction Center of Excellence. The simulation results show that the average absolute errors of the state of health prediction for echelon utilization batteries are less than 0.8% after the deep neural network learning prediction model is modified by Markov chain error, which provides a theoretical basis for the safe and stable operation of echelon utilization batteries. 

State of health assessment for echelon utilization batteries based on deep neural network learning with error correction

Equilibria investigation and extraction thermodynamic of extracting (o-/m-/p-)-cresol from aqueous solution using 1-dodecanol

In this work, liquid–liquid equilibrium (LLE) data for the ternary systems (water + propionic acid + solvent) were experimentally obtained at atmospheric pressure and 2982 K The ternary systems show type-1 behavior of LLE Cyclopentane, cyclopentanol, 2-octanone, and dibutyl maleate were chosen as solvent and it has been noted that there are no data in the literature on these ternary systems The consistency of the experimental tie-line data was checked using the Hand and Othmer-Tobias correlation equations A comparison of the extracting capabilities of the solvent was made with respect to the distribution coefficients and separation factors The correlation of the experimental tie-line data was confirmed by the NRTL thermodynamic model A Group Method of Data Handling (GMDH)-type neural network (NN) was also used to correlate the experimental tie-lines It is shown that the results of the both models cohere with the experimental values

Sezin Bekri

Papers

Correlation of Experimental Liquid–Liquid Equilibrium Data for Ternary Systems Using NRTL and GMDH-Type Neural Network

Application of deep neural network (DNN) for experimental liquid-liquid equilibrium data of water + butyric acid + 5-methyl-2-hexanone ternary systems