Abstract: A controller determines and adjusts system parameters, including cleanliness levels or sootblower operating settings, that are useful for maintaining the cleanliness of a fossil fuel boiler at an efficient level. Some embodiments use a direct controller to determine cleanliness levels and/or sootblower operating settings. Some embodiments use an indirect controller, with a system model, to determine cleanliness levels and/or sootblower settings. The controller may use a model that is, for example, a neural network, or a mass energy balance, or a genetically programmed model. The controller uses input about the actual performance or state of the boiler for adaptation. The controller may operate in conjunction with a sootblower optimization system that controls the actual settings of the sootblowers. The controller may coordinate cleanliness settings for multiple sootblowers and/or across a plurality of heat zones in the boiler.

Abstract: A controller determines and adjusts system parameters, including cleanliness levels or sootblower operating settings, that are useful for maintaining the cleanliness of a fossil fuel boiler at an efficient level. Some embodiments use a direct controller to determine cleanliness levels and/or sootblower operating settings. Some embodiments use an indirect controller, with a system model, to determine cleanliness levels and/or sootblower settings. The controller may use a model that is, for example, a neural network, or a mass energy balance, or a genetically programmed model. The controller uses input about the actual performance or state of the boiler for adaptation. The controller may operate in conjunction with a sootblower optimization system that controls the actual settings of the sootblowers. The controller may coordinate cleanliness settings for multiple sootblowers and/or across a plurality of heat zones in the boiler.

Abstract: A controller determines and adjusts system parameters, including cleanliness levels or sootblower operating settings, that are useful for maintaining the cleanliness of a fossil fuel boiler at an efficient level. Some embodiments use a direct controller to determine cleanliness levels and/or sootblower operating settings. Some embodiments use an indirect controller, with a system model, to determine cleanliness levels and/or sootblower settings. The controller may use a model that is, for example, a neural network, or a mass energy balance, or a genetically programmed model. The controller uses input about the actual performance or state of the boiler for adaptation. The controller may operate in conjunction with a sootblower optimization system that controls the actual settings of the sootblowers. The controller may coordinate cleanliness settings for multiple sootblowers and/or across a plurality of heat zones in the boiler.

Abstract: Various embodiments of the invention are directed to methods and systems for assigning credit to the external and intermediate inputs of an enterprise-level system or other aggregate process with one or more global outputs that is composed of a number of interconnected local processes. Assigning credit is a mechanism for evaluating the impact of a particular variable, e.g., an input to one of the local processes, on the final output of the aggregate process. In certain embodiments of the invention, credit is assigned to the local inputs of each local process in two steps. First, for each local input, a local credit assignment is obtained. For chained outputs, credit assignment data is provided as calculated for the later stage processes to which the outputs are chained. Second, a global credit assignment is derived for the local inputs from the local credit assignment and the credit assignment information from later stage processes.

Abstract: A controller determines and adjusts system parameters, including cleanliness levels or sootblower operating settings, that are useful for maintaining the cleanliness of a fossil fuel boiler at an efficient level, Some embodiments use a direct controller to determine cleanliness levels and/or sootblower operating settings. Some embodiments use an indirect controller, with a system model, to determine cleanliness levels and/or sootblower settings. The controller may use a model that is, for example, a neural network, or a mass energy balance, or a genetically programmed model. The controller uses input about the actual performance or state of the boiler for adaptation. The controller may operate in conjunction with a sootblower optimization system that controls the actual settings of the sootblowers. The controller may coordinate cleanliness settings for multiple sootblowers and/or across a plurality of heat zones in the boiler.

Abstract: A controller determines and adjusts system parameters, including cleanliness levels or sootblower operating settings, that are useful for maintaining the cleanliness of a fossil fuel boiler at an efficient level. Some embodiments use a direct controller to determine cleanliness levels and/or sootblower operating settings. Some embodiments use an indirect controller, with a system model, to determine cleanliness levels and/or sootblower settings. The controller may use a model that is, for example, a neural network, or a mass energy balance, or a genetically programmed model. The controller uses input about the actual performance or state of the boiler for adaptation. The controller may operate in conjunction with a sootblower optimization system that controls the actual settings of the sootblowers. The controller may coordinate cleanliness settings for multiple sootblowers and/or across a plurality of heat zones in the boiler.

Abstract: A method and system are provided for calculating a marginal cost curve for a plant at a plant load range comprising a plurality of specified plant loads. The method includes providing a model used for controlling the plant. The model relates plant load and one or more given cost contributing factors. The marginal value for each of the one or more given cost contributing factors at each of the plurality of specified plant loads is determined using the model. A variable cost at each of the plurality of specified plant loads is determined by: (i) multiplying the marginal value of each of the one or more given cost contributing factors at each of the plurality of specified plant loads by a respective unit cost of each of the one or more given cost contributing factors, and (ii) if there are a plurality of given cost contributing factors, summing the results of (i) at each of the plurality of specified plant loads.

Abstract: A controller determines and adjusts system parameters, including cleanliness levels or sootblower operating settings, that are useful for maintaining the cleanliness of a fossil fuel boiler at an efficient level. Some embodiments use a direct controller to determine cleanliness levels and/or sootblower operating settings. Some embodiments use an indirect controller, with a system model, to determine cleanliness levels and/or sootblower settings. The controller may use a model that is, for example, a neural network, or a mass energy balance, or a genetically programmed model. The controller uses input about the actual performance or state of the boiler for adaptation. The controller may operate in conjunction with a sootblower optimization system that controls the actual settings of the sootblowers. The controller may coordinate cleanliness settings for multiple sootblowers and/or across a plurality of heat zones in the boiler.

Abstract: A system and method for implementing an indirect controller for a plant. A plant can be provided with both a direct controller and an indirect controller with a system model or a committee of system models. When the system model has sufficient integrity to satisfy the plant requirements, i.e., when the system model has been sufficiently trained, the indirect controller with the system model is automatically enabled to replace the direct controller. When the performance falls, the direct controller can automatically assume operation of the plant, preferably maintaining operation in a control region suitable for generating additional training data for the system model. Alternatively, the system model incorporates a committee of models. Various types of sources for errors in the committee of models can be detected and used to implement strategies to improve the quality of the committee.

Abstract: A controller determines and adjusts system parameters, including cleanliness levels or sootblower operating settings, that are useful for maintaining the cleanliness of a fossil fuel boiler at an efficient level. Some embodiments use a direct controller to determine cleanliness levels and/or sootblower operating settings. Some embodiments use an indirect controller, with a system model, to determine cleanliness levels and/or sootblower settings. The controller may use a model that is, for example, a neural network, or a mass energy balance, or a genetically programmed model. The controller uses input about the actual performance or slate of the boiler for adaptation. The controller may operate in conjunction with a sootblower optimization system that controls the actual settings of the sootblowers. The controller may coordinate cleanliness settings for multiple sootblowers and/or across a plurality of heat zones in the boiler.