A method for predicting a yield of calcium in a calcium treatment process based on deep neural network as provided relates to a calcium treatment process of molten steel refining in the field of iron and steel metallurgy, and includes steps of: obtaining production and operation data information of each of charges and thereby constructing a dataset; training and testing a deep neural network based on constructed dataset to establish a prediction model; and based on the prediction model, predicting and calculating current yield of calcium by taking actual production and operation data information of each charge as input. The method can predict the yield of calcium in the calcium treatment process, is favorable for accurately controlling a calcium content of steel, can stably control the calcium treatment process, improve the calcium treatment effect, improve the product quality, and ensure the production stability.

A processor executable method, system, and computer-readable media expedite the process of entering equations for use in developing simulations of chemical processes. The process of entering equations is expedited by dynamically inferring the dimensions of variables. The process infers the dimensions of all variables in user-added equations, and infers the dimensions of each variable in a user-added equation sequentially. The process automatically creates variables with unassigned dimensions in response to indications, such as inputs from a user, to declare new equations. The process assigns dimensions to variables based on relations between variables, such as logical relations between the dimensions of variables.

A computer implemented method for designing chemical reactions for catalyst construction is described. The method includes extracting historical data including historic chemical reaction data and historic catalyst construction yield data and converting the historic chemical reaction data into graph models to represent molecular structure data. The method also includes incorporating the graph models into a chemical reaction algorithm and training a vectorized cognitive deep learning network of the chemical reaction algorithm by using the graph models and a property of the historic chemical reaction data to produce a catalyst chemical reaction model. Further, the method includes validating the catalyst chemical reaction model by inputting the historic chemical reaction data and comparing a generated property corresponding to the catalyst chemical reaction model to the property of the historic chemical reaction data. Lastly, the method includes updating the training of the catalyst chemical reaction model.

A computer implemented method for designing chemical reactions for catalyst construction is described. The method includes extracting historical data including historic chemical reaction data and historic catalyst construction yield data and converting the historic chemical reaction data into graph models to represent molecular structure data. The method also includes incorporating the graph models into a chemical reaction algorithm and training a vectorized cognitive deep learning network of the chemical reaction algorithm by using the graph models and a property of the historic chemical reaction data to produce a catalyst chemical reaction model. Further, the method includes validating the catalyst chemical reaction model by inputting the historic chemical reaction data and comparing a generated property corresponding to the catalyst chemical reaction model to the property of the historic chemical reaction data. Lastly, the method includes updating the training of the catalyst chemical reaction model.

An automated chemical formulation apparatus includes: a data reception unit that receives an input chemical material dataset including chemical material information, chemical composition information, chemical formulation information and property information thereof; a predicting model generation unit that trains a first machine learning model using the input chemical material dataset to generate a predicting model for predicting a chemical formulation based on target property information of a target material; and a formulation prediction unit that sets a boundary condition based on the input chemical material dataset, generates a new input dataset including at least one of chemical material information, chemical composition information, and chemical formulation information within the boundary condition, inputs the new input dataset to the predicting model, and sets predetermined one or more pieces of target property information to perform prediction, thereby outputting a first group of chemical formulation data.

The present disclosure provides a method of generating compound information in a computing apparatus, the method including obtaining a learning model for information associated with partial structures, obtaining information associated with a source molecule that is a target of a partial structure modification, obtaining information associated with a partial structure set including a plurality of partial structures of the source molecule, selecting, from the partial structures included in the partial structure set, a target partial structure to be modified, obtaining, using the learning model, information associated with a modified partial structure corresponding to the target partial structure, and outputting result information in which the target partial structure is replaced by the modified partial structure in the source molecule.

The present disclosure provides a method of generating compound information in a computing apparatus, the method including obtaining a learning model for information associated with partial structures, obtaining information associated with a source molecule that is a target of a partial structure modification, obtaining information associated with a partial structure set including a plurality of partial structures of the source molecule, selecting, from the partial structures included in the partial structure set, a target partial structure to be modified, obtaining, using the learning model, information associated with a modified partial structure corresponding to the target partial structure, and outputting result information in which the target partial structure is replaced by the modified partial structure in the source molecule.

A sample analysis device includes an acquirer that acquires quantitative information of a test substance present in a sample, an estimator that reads a generalized reaction model obtained by generalization of a plurality of reaction models from a storage device and estimates a posterior distribution of a parameter of the generalized reaction model using Bayesian inference, and a calculator that calculates a confidence interval or a quantile of the quantitative information of a test substance in any period of time or calculates a confidence interval of a quantile in a period of time until the quantitative information of a test substance reaches a predetermined specification limit, based on the posterior distribution of a parameter estimated by the estimator.

In one aspect, a method is disclosed for using dialects to generate candidate drug compounds. The dialects describe sequences of the candidate drug compounds and activities associated with the sequences. The method includes receiving a data set, training, using the data set, first layers of a machine learning model to determine relationships of components of a portion of a string described by a first dialect. The components pertain to amino acids associated with first activity level information of the sequences. The method includes training, using the data set and the portion of the string, a final layer to generate a remainder of the string. The remainder pertains to second activity level information of the sequences. The method includes generating, using the first and final layer, the string comprising the portion and the remainder. The string represents a candidate drug compound.

A method for analyzing or optimizing the operation of waste incinerator systems. The content of CO2 is measured in the exhaust gas and is used to determine the ratio of biogenic carbon to fossil carbon in the incinerated waste, if necessary after resetting to the CO2 reference quantity. The variability of the CO2 reference or the ratio of biogenic carbon to fossil carbon in the incinerated waste is determined and recorded according to quantity and duration. When optimizing the operation, the location of the waste in the bunker, from which the incinerated waste originates with a composition or variability that has now been ascertained using the method, is used to further remove or mix the waste.