Abstract: In an example method, a system accesses a first data set representing parameters for implementing and operating a Power-to-X plant, and determines one or more configurations of the Power-to-X plant based on the first data set and a Bayesian optimization process. The configurations are determined by parsing the first data set to identify fields in the first data set, each representing a respective parameter; generating a plurality of data vectors representing respective first candidate configurations of the Power-to-X plant; inputting the data vectors and the first data set into a computer model; predicting, based on the computer model, a performance of the Power-to-X plant according to each of the first candidate configurations; and generating, using the Bayesian optimization process, one or more second candidate configurations based on the predicted performance. Further, the system generates and stores a second data set representing the one or more configurations of the Power-to-X plant.

Abstract: In an example method, a system accesses a first data set representing parameters for implementing and operating a Power-to-X plant, and determines one or more configurations of the Power-to-X plant based on the first data set and a Bayesian optimization process. The configurations are determined by parsing the first data set to identify fields in the first data set, each representing a respective parameter; generating a plurality of data vectors representing respective first candidate configurations of the Power-to-X plant; inputting the data vectors and the first data set into a computer model; predicting, based on the computer model, a performance of the Power-to-X plant according to each of the first candidate configurations; and generating, using the Bayesian optimization process, one or more second candidate configurations based on the predicted performance. Further, the system generates and stores a second data set representing the one or more configurations of the Power-to-X plant.

Abstract: In an example method, a system accesses a first data set representing parameters for implementing and operating a Power-to-X plant, and determines one or more configurations of the Power-to-X plant based on the first data set and a Bayesian optimization process. The configurations are determined by parsing the first data set to identify fields in the first data set, each representing a respective parameter; generating a plurality of data vectors representing respective first candidate configurations of the Power-to-X plant; inputting the data vectors and the first data set into a computer model; predicting, based on the computer model, a performance of the Power-to-X plant according to each of the first candidate configurations; and generating, using the Bayesian optimization process, one or more second candidate configurations based on the predicted performance. Further, the system generates and stores a second data set representing the one or more configurations of the Power-to-X plant.