System and method for predicting shutdown alarms in boiler using machine learning
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Solution Overview
Problem
Conventional boiler systems face inefficiencies in operation and frequent unanticipated shutdowns due to lack of experience among operators and reliance on limited user interface data, leading to difficulties in monitoring and controlling boiler performance, especially in remote locations.
Innovation Solution
Implementing a system that utilizes machine learning models and IoT devices to sense and predict shutdown alarms by collecting and processing data from various parameters, including firing rate, oxygen levels, and water temperature, and sending alerts to operators to prevent shutdowns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional boiler systems rely on operator experience and basic user interface data, then the system structure remains simple, but shutdown frequency increases and operational efficiency decreases
Solution Approach 1:
The machine learning model performs preliminary analysis of boiler parameters to predict potential shutdown conditions before they occur. By analyzing historical and real-time data patterns, the system identifies early warning signs of problematic conditions, allowing operators to take preventive actions before shutdowns occur, thus improving reliability without requiring complete system redesign
Solution Approach 2:
The patent introduces an intermediary machine learning prediction system that sits between the basic user interface and the operator. This intermediary layer processes raw boiler parameters, applies predictive algorithms, and presents simplified risk assessments to operators, thereby improving shutdown prediction capability without directly increasing the complexity visible to end users
2Object-affected harmful factors
If operators are located remotely from boilers, then safety is improved, but monitoring capability and response time deteriorate
Solution Approach 1:
The system implements enhanced feedback mechanisms that provide operators with predictive insights and detailed boiler status information remotely. The machine learning model continuously monitors parameters and provides actionable feedback about potential issues, enabling operators to maintain safety at a distance while still having comprehensive monitoring capabilities through digital information streams
Solution Approach 2:
The patent replaces the need for physical proximity to boilers with automated digital monitoring and prediction systems. Sensors, communication networks, and machine learning algorithms substitute for the mechanical advantage of being physically near the equipment, allowing operators to safely remain remote while maintaining superior monitoring and response capabilities through automated systems
3Device complexity
If manual intervention is required for alarm reset and boiler restart, then system simplicity is maintained, but productivity and response time decrease
Solution Approach 1:
The machine learning model performs preliminary assessment of alarm conditions to determine whether they are resolvable through automated actions or require manual intervention. By predicting the nature and severity of issues before they trigger full shutdowns, the system enables automated responses for routine conditions, freeing up productivity while maintaining operator involvement for complex situations
Solution Approach 2:
The system implements self-service capabilities where the machine learning model automatically analyzes alarm conditions, identifies root causes, and executes appropriate corrective actions without requiring manual operator intervention. This self-diagnosis and self-correction functionality increases productivity by reducing downtime for common issues while maintaining system simplicity through rule-based automated responses
Data Source
AI summary
Systems and methods for anticipating shutdown alarms for a boiler system by way of one or more machine learning (ML) or artificial intelligence (AI) models are disclosed herein. In an example embodiment, a method for anticipating shutdown alarms with respect to a boiler by way of a ML model includes receiving and storing, at one or more storage devices, a plurality of types of boiler-related data that are received at least indirectly from a plurality of internet of things (IoT) devices. The method also includes preprocessing and feature engineering the plurality of types of boiler-related data to arrive at a training data set, training the ML model, and deploying the trained model. The method further includes receiving additional boiler-related data concerning the boiler and, by way of the model, determining an alarm prediction concerning an anticipated alarm, and taking at least one action based at least upon the prediction.