A method for controlling a chemical process, by preparing methanol, hydrogen sulfide, methyl mercaptan, hydrocyanic acid, acrolein, 3-methylthiopropionaldehyde, 5-(2-methylmercaptoethyl)-hydantoin, methionine, a salt of methionine, and a derivative of methionine. The method includes providing a training set TS1, wherein TS1 is process values PV1 and process values PV2 being correlated to one another, and/or laboratory values LV1 and process values PV2 being correlated to one another. The method includes training a processing unit on the training set TS1 to identify a pattern of correlation between one or more measured process variables and at least one process variable. The method includes developing a calibration function CF1 for a calibrated soft sensor from the identified pattern of correlation and predicting at least one operating parameter for the chemical process as an approximation to LV1 and/or PV1. A system for controlling a chemical process.

A method for controlling a chemical process, by preparing methanol, hydrogen sulfide, methyl mercaptan, hydrocyanic acid, acrolein, 3-methylthiopropionaldehyde, 5-(2-methylmercaptoethyl)-hydantoin, methionine, a salt of methionine, and a derivative of methionine. The method includes providing a training set TS1, wherein TS1 is process values PV1 and process values PV2 being correlated to one another, and/or laboratory values LV1 and process values PV2 being correlated to one another. The method includes training a processing unit on the training set TS1 to identify a pattern of correlation between one or more measured process variables and at least one process variable. The method includes developing a calibration function CF1 for a calibrated soft sensor from the identified pattern of correlation and predicting at least one operating parameter for the chemical process as an approximation to LV1 and/or PV1. A system for controlling a chemical process.

This disclosure provides new methods for the design and development of ethylene polymerization catalysts, including Group 4 metallocene catalysts such as zirconocenes, which are based on an improved ability to adjust co-monomer incorporation into the polymer. Computational analyses with and without dispersion corrections revealed that designing catalyst scaffolds which induce stabilizing non-covalent dispersion type interactions with incoming α-olefin co-monomers can be used to modulate co-monomer selectivity into the polyethylene chain. Demonstrated herein is a lack of correlation of computed ΔΔG.sup.‡ values against experimental ΔΔG.sup.‡ values when the dispersion correction (D3BJ) was disabled, and B3LYP was used in the absence of Grimme's D3 dispersion and Becke-Johnson (BJ) dampening, but a correlation of computed against experimental ΔΔG.sup.‡ with B3LYP+D3BJ, which are used to design new catalyst scaffolds.

Machine vision technology can be used to predict a property of a product generated by a chemical process. The prediction can be based on an analytical characterization of the chemical process or the product generated by the chemical process with a detector that generates series data. The series data can be converted to an image and input to an artificial neural network (ANN) trained to predict the property of the product based on the image. A prediction of a property of the product can be received from the ANN and used to adjust the chemical process or to determine whether to reject the product.

Machine vision technology can be used to predict a property of a product generated by a chemical process. The prediction can be based on an analytical characterization of the chemical process or the product generated by the chemical process with a detector that generates series data. The series data can be converted to an image and input to an artificial neural network (ANN) trained to predict the property of the product based on the image. A prediction of a property of the product can be received from the ANN and used to adjust the chemical process or to determine whether to reject the product.

Disclosed are a method and apparatus of predicting properties of feed and products in a reformer. The method of predicting properties of feed and products in a reformer includes training a first predictive model for predicting the properties of feed in the reformer and a second predictive model for predicting the properties of products in the reformer; predicting the properties of feed being currently supplied to the reactor in real time by allowing a first prediction unit including the trained first prediction model to receive a current operating condition of the reactor in the reformer; and predicting the properties of products being produced in the reactor in real time by allowing a second prediction unit including the trained second prediction model to receive the current operating condition and the predicted properties of feed.

Disclosed are a method and apparatus of predicting properties of feed and products in a reformer. The method of predicting properties of feed and products in a reformer includes training a first predictive model for predicting the properties of feed in the reformer and a second predictive model for predicting the properties of products in the reformer; predicting the properties of feed being currently supplied to the reactor in real time by allowing a first prediction unit including the trained first prediction model to receive a current operating condition of the reactor in the reformer; and predicting the properties of products being produced in the reactor in real time by allowing a second prediction unit including the trained second prediction model to receive the current operating condition and the predicted properties of feed.

Provided are a device and method for predicting product properties of a naphtha splitting unit (NSU). The method includes training a prediction model for predicting the product properties of the NSU, inputting an input variable to the trained prediction model to acquire a prediction value for each output variable, and outputting the acquired prediction values for the output variables.

A method and system for in-silico testing of actives on human skin is described. The present invention discloses a micro and macroscopic level model of the skins upper protective layer Stratum-Corneum. The invention presents a multi-scale modeling framework for the calculation of diffusion and release profile of different actives like drugs, particles and cosmetics through developed skin model using molecular dynamics simulations and computational fluid dynamics approach. The systems consist of a molecular model of the skin's upper layer stratum corneum and permeate molecules. The system also consists of a macroscopic transport model of stratum corneum. The transport model is used to generate the release profile of the active molecule.

A method and system for in-silico testing of actives on human skin is described. The present invention discloses a micro and macroscopic level model of the skins upper protective layer Stratum-Corneum. The invention presents a multi-scale modeling framework for the calculation of diffusion and release profile of different actives like drugs, particles and cosmetics through developed skin model using molecular dynamics simulations and computational fluid dynamics approach. The systems consist of a molecular model of the skin's upper layer stratum corneum and permeate molecules. The system also consists of a macroscopic transport model of stratum corneum. The transport model is used to generate the release profile of the active molecule.