SURROGATE MODEL FOR A CHEMICAL PRODUCTION PROCESS

    公开(公告)号:US20200167647A1

    公开(公告)日:2020-05-28

    申请号:US16662460

    申请日:2019-10-24

    Abstract: Aspects of the technology described herein comprise a surrogate model for a chemical production process. A surrogate model is a machine learned model that uses a collection of inputs and outputs from a simulation of the chemical production process and/or actual production data as training data. Once trained, the surrogate model can estimate an output of a chemical production process given an input to the process. Surrogate models are not directly constrained by physical conditions in a plant. This can cause them to suggest optimized outputs that the not possible to produce in the real world. It is a significant challenge to train a surrogate model to only produce outputs that are possible. The technology described herein improves upon previous surrogate models by constraining the output of the surrogate model to outputs that are possible in the real world.

    RAW MATERIAL EVALUATION PROCESS
    2.
    发明申请

    公开(公告)号:US20200184401A1

    公开(公告)日:2020-06-11

    申请号:US16705899

    申请日:2019-12-06

    Abstract: Aspects of the technology described herein comprise a raw material valuation system that is able to quantify an outcome of various raw material management decisions. Raw material management decisions can include, but are not limited to, purchasing a raw material, selling a raw material, transferring a raw material within a chemical production system, and substituting a proposed purchase of a first raw material with the purchase of a second material. The raw material valuation system can quantify a contemplated changes to a raw material management plan by comparing an optimal reference usage plan to an optimal updated usage plan. The raw material valuation system can calculate a breakeven sale price for a raw material in inventory or a breakeven purchase price for a raw material to be purchased. The raw material valuation system used to generate the reference usage plan and the updated usage plan can use a multi-period optimization model.

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