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.

A method for setting conditions for use of a polymerization catalyst includes a step of acquiring a physical property value derived from remaining functional groups after maintaining a temperature of a composition including a polymerization-reactive compound and a predetermined amount of a polymerization catalyst, a step of calculating a remaining functional group ratio from the physical property value, a step of calculating a reaction rate constant based on a reaction rate equation from the remaining functional group ratio, a step of calculating an activation energy and a frequency factor from the reaction rate constant using an Arrhenius plot, a step of determining whether or not the activation energy satisfies a predetermined condition for the polymerization catalyst, an step of setting an approximation equation from the frequency factor, and a step of setting an addition range with respect to the polymerization-reactive compound.

A control device performs control of a culture condition in production of an organic compound by a fermentation method. The control device executes processing a plurality of times to acquire culture data, to calculate, using the acquired culture data, a plurality of candidates for a culture condition set in advance, and a linear model set in advance that outputs a production amount of an organic compound at a future time, the production amount at the future time for each of the candidates, to determine an optimum candidate out of the candidates using the calculated production amount at the future time for each of the candidates and a target production amount of the organic compound at the future time set in advance, and to change the culture condition to the determined candidate. The linear model and the target production amount are set for each time.

Aspects of the present disclosure relate to systems and methods for manufacturing biologically-produced pharmaceutical products. Some of the systems described herein comprise an upstream component comprising a bioreactor and at least one filter (e.g., a filter probe) integrated with a downstream component comprising a purification module comprising at least a first partitioning unit and a second partitioning unit. In some embodiments; these integrated biomanufacturing systems may be operated under continuous or conditions and may be capable of efficiently producing pure, high-quality pharmaceutical products.

In some embodiments, a method includes receiving a set of target attributes associated with a chemical product formulation and a set of priority values of the plurality of target attributes. The method includes determining, based on (1) a first neural network, (2) the set of target attributes and (3) the set of priority values, a set of sample formulations. The method includes determining a set of scores based on the set of sample formulations. The method includes selecting, based on the set of scores and the set of target attributes, a sample formulation from the set of sample formulations having a score greater than remaining scores from the set of scores. The method includes determining an origin associated with the sample formulation. When the origin is included in a pre-determined group, the method includes generating a report including the sample formulation as the chemical product formulation.

In some embodiments, a method includes receiving a set of target attributes associated with a chemical product formulation and a set of priority values of the plurality of target attributes. The method includes determining, based on (1) a first neural network, (2) the set of target attributes and (3) the set of priority values, a set of sample formulations. The method includes determining a set of scores based on the set of sample formulations. The method includes selecting, based on the set of scores and the set of target attributes, a sample formulation from the set of sample formulations having a score greater than remaining scores from the set of scores. The method includes determining an origin associated with the sample formulation. When the origin is included in a pre-determined group, the method includes generating a report including the sample formulation as the chemical product formulation.

Disclosed is a method for predicting at least one aspect of an enzymatically catalyzed chemical reaction. The method comprises providing a trained machine learning model, and inputting one or two input strings into the training model. Each input string is selected from a group of strings consisting of: a string representation of at least one educts of the chemical reaction, a string representation of at least one product of the chemical reaction, and/or a string representation of amino acids of an enzyme which is supposed to transform the educts into the products in the reaction. The trained machine learning model predicts at least the one or more strings which were not provided as input and the prediction is performed as a function of the one or two strings provided as input. The method outputting the prediction result for predicting or optimizing the chemical reaction.

Disclosed is a method for predicting at least one aspect of an enzymatically catalyzed chemical reaction. The method comprises providing a trained machine learning model, and inputting one or two input strings into the training model. Each input string is selected from a group of strings consisting of: a string representation of at least one educts of the chemical reaction, a string representation of at least one product of the chemical reaction, and/or a string representation of amino acids of an enzyme which is supposed to transform the educts into the products in the reaction. The trained machine learning model predicts at least the one or more strings which were not provided as input and the prediction is performed as a function of the one or two strings provided as input. The method outputting the prediction result for predicting or optimizing the chemical reaction.

An objective of the present invention is to provide a document creation assistance server and document creation assistance method which are capable of efficiently acquiring information necessary to write a specification or other such documents, and of generating a document from the acquired information. According to an embodiment, this document creation assistance server for assisting with the creation of a document comprises: a final product identification part for accepting identification information for identifying a final product generated by synthesizing a plurality of chemical compounds; a chemical equation acquisition part for referring to electronic experiment note data which provides an electronic record of the details of an experiment relating to the synthesis of the chemical compounds, and acquiring a chemical equation for generating the final product associated with the accepted identification information; and a document generation part for generating a document including the acquired chemical equation.

An objective of the present invention is to provide a document creation assistance server and document creation assistance method which are capable of efficiently acquiring information necessary to write a specification or other such documents, and of generating a document from the acquired information. According to an embodiment, this document creation assistance server for assisting with the creation of a document comprises: a final product identification part for accepting identification information for identifying a final product generated by synthesizing a plurality of chemical compounds; a chemical equation acquisition part for referring to electronic experiment note data which provides an electronic record of the details of an experiment relating to the synthesis of the chemical compounds, and acquiring a chemical equation for generating the final product associated with the accepted identification information; and a document generation part for generating a document including the acquired chemical equation.