Abstract: Unique systems, methods, techniques and apparatuses of an alarm management system are disclosed herein. One exemplary embodiment is a method for monitoring an industrial plant comprising determining a sequence of alarm events for each of a plurality of time intervals including a first alarm event of a plurality of alarm events and a second alarm event of the plurality of alarm events; determining a count of the first alarm events and second alarm events; determining the alarm events exceed a support threshold value; determining a third count of a sub-sequence of the sequences of alarm events including the first alarm event followed by the second alarm event in response to determining the first count and the second count exceeds the support threshold value; determining a ratio using the first count, the second count, and the third count exceeds a display threshold value; and displaying the sub-sequence.

Abstract: Unique systems, methods, techniques and apparatuses of an alarm management system are disclosed herein. One exemplary embodiment is a method for monitoring an industrial plant comprising determining a sequence of alarm events for each of a plurality of time intervals including a first alarm event of a plurality of alarm events and a second alarm event of the plurality of alarm events; determining a count of the first alarm events and second alarm events; determining the alarm events exceed a support threshold value; determining a third count of a sub-sequence of the sequences of alarm events including the first alarm event followed by the second alarm event in response to determining the first count and the second count exceeds the support threshold value; determining a ratio using the first count, the second count, and the third count exceeds a display threshold value; and displaying the sub-sequence.

Abstract: Multiple models for various stages of a non-linear process control are developed by clustering perturbation data obtained from the nonlinear process so as to permit multiple local data regions to be identified as a function of substantial similarity between the data, wherein the data of first data set represent the non-linear process. A discrete model corresponding to each of the local data regions is generated. The number of the discrete models may be reduced as a function of prediction error between actual outputs of the process and predicted outputs of the models and as a function of a gap metric based on closed loop similarity and frequency response similarity between the models.

Abstract: A process for optimizing a portfolio of products produced from a crop includes the use of an objective function to determine optimized quantities of the products in the portfolio. The objective function, for example, includes quantity terms for the products. The objective function may also include additional terms such as an energy term and/or a storage term. The energy term, for example, relates to an amount of energy required to produce the products. The storage term, for example, relates to the cost of storing products. The crop, for example, may be sugarcane, and the products, for example, may be sugar, molasses, bagasse, biofuel, electricity, and/or carbon credits.

Abstract: A product recovery prediction model that models recovery of a product from a crop is generated by inputting training product recovery data by date, age, and variety. A first model that models season dependent effects on product recovery, and/or a second model that models age dependent effects on product recovery, and/or a third model that models other effects such as, for example, weather dependent effects on product recovery are generated. The first, second, and/or third models are combined, and the product recovery prediction model is generated based on the combined first, second, and/or third models and on the training product recovery data. The crop may be sugarcane, and the product may be sugar. The product recovery prediction model may be used to predict recovery of the product to use for harvesting or any economical decisions.

Abstract: A process for optimizing a portfolio of products produced from a crop includes the use of an objective function to determine optimized quantities of the products in the portfolio. The objective function, for example, includes quantity terms for the products. The objective function may also include additional terms such as an energy term and/or a storage term. The energy term, for example, relates to an amount of energy required to produce the products. The storage term, for example, relates to the cost of storing products. The crop, for example, may be sugarcane, and the products, for example, may be sugar, molasses, bagasse, biofuel, electricity, and/or carbon credits.

Abstract: Multiple models for various stages of a non-linear process control are developed by clustering perturbation data obtained from the nonlinear process so as to permit multiple local data regions to be identified as a function of substantial similarity between the data, wherein the data of first data set represent the non-linear process. A discrete model corresponding to each of the local data regions is generated. The number of the discrete models may be reduced as a function of prediction error between actual outputs of the process and predicted outputs of the models and as a function of a gap metric based on closed loop similarity and frequency response similarity between the models.

Abstract: A combination of yield prediction models is usable to predict the yield of a crop, such as sugarcane, from land. The model combination includes at least first and/or second models. The first model may be a structured or unstructured model that models season dependent effects on yield. If structured, the first model may be a linear, non-linear, or polynomial representation. The second model may be a structured or unstructured model that models age dependent effects on yield. If structured, the second model may be a linear, non-linear, or polynomial representation. Additional models that model weather and/or soil dependent effects on yield may also be used in the model combination.

Abstract: A product recovery prediction model that models recovery of a product from a crop is generated by inputting training product recovery data by date, age, and variety. A first model that models season dependent effects on product recovery, and/or a second model that models age dependent effects on product recovery, and/or a third model that models other effects such as, for example, weather dependent effects on product recovery are generated. The first, second, and/or third models are combined, and the product recovery prediction model is generated based on the combined first, second, and/or third models and on the training product recovery data. The crop may be sugarcane, and the product may be sugar. The product recovery prediction model may be used to predict recovery of the product to use for harvesting or any economical decisions.