Achieved 70% Reduction in downtime of conveyor failures with Ripik AI conveyor belt monitoring system. This ensured smoother operations, minimized downtime, and delivered significant cost savings.
Manual inspections frequently overlooked early signs of refractory damage, resulting in unplanned downtime. By contrast, AI-driven monitoring enables timely detection of wear, cutting downtime by 50% and enhancing operational reliability.
Reducing inconsistencies in calorific values through AFR monitoring optimizes combustion efficiency, minimizes equipment damage, and lowers energy consumption.
Achieved an 82% reduction in crusher blockages and a 20% decrease in unwanted stoppages in cement crusher operations by detecting oversized rocks in real time. This led to smoother operations, reduced downtime, and substantial cost savings.
Real-time camera intelligence to monitor materials, equipment, and processes, driving efficiency and consistency in industrial operations.
Hosts and manages hundreds of Vision AI Agents, enabling you to customize, deploy, and scale real-time monitoring solutions instantly across operations.
Let us walk you through a tailored demo experience.
Achieved 70% Reduction in downtime of conveyor failures with Ripik AI conveyor belt monitoring system. This ensured smoother operations, minimized downtime, and delivered significant cost savings.
Manual inspections frequently overlooked early signs of refractory damage, resulting in unplanned downtime. By contrast, AI-driven monitoring enables timely detection of wear, cutting downtime by 50% and enhancing operational reliability.
Reducing inconsistencies in calorific values through AFR monitoring optimizes combustion efficiency, minimizes equipment damage, and lowers energy consumption.
Achieved an 82% reduction in crusher blockages and a 20% decrease in unwanted stoppages in cement crusher operations by detecting oversized rocks in real time. This led to smoother operations, reduced downtime, and substantial cost savings.
Real-time camera intelligence to monitor materials, equipment, and processes, driving efficiency and consistency in industrial operations.
Hosts and manages hundreds of Vision AI Agents, enabling you to customize, deploy, and scale real-time monitoring solutions instantly across operations.
Let us walk you through a tailored demo experience.
The cement industry is one of the largest sectors and serves as the backbone of construction and infrastructure development. It is constantly seeking ways to reduce operational costs and minimize its environmental footprint. Traditionally, the cement industry has relied on fossil fuels, coal, and coke as primary fuels for kilns, which are associated with high carbon emissions. In line with government policies across the globe aimed at reducing emissions, the adoption of alternative fuels has become a critical solution.
Alternative fuels, such as Refuse-Derived Fuel (RDF), a type of solid waste, are increasingly being considered a viable solution. They serve as an alternative fuel option and an efficient method for disposing of municipal solid waste. The unique properties of cement sintering in rotary kilns allow for the use of various fuels, including some that are prohibited in other processes. However, despite the environmental and economic benefits, there are key challenges associated with burning RDF in cement kilns. To tackle these challenges, advanced technological solutions, notably AI vision systems and analytical tools are required. Let’s explore the impact of adopting such fuels.
Alternative fuels like RDF or municipal solid waste are environmentally friendly unlike traditional fossil fuels. However, their inherent complexity poses challenges in using them as fuels. Here are some of them:
RDF is a mixture of various waste materials, which may include PVC and materials with higher chloride and sulfur content—resulting in a low melting point.
This unrefined mixture creates more complex chemical and physical properties compared to traditional fuels, making it difficult to determine the exact calorific value.
The complex mixture of different materials with varying calorific values in the kiln can result in incomplete combustion, uneven heat distribution, and unstable clinker formation. If not addressed, these issues can cause severe blockages and ring formations within the cement kiln, leading to operational inefficiencies, reduced clinker quality, and negative environmental impacts.
For RDF to be a suitable alternative to fossil fuels, it must meet certain high-quality standards to be used in cement kilns. Otherwise, it may have adverse effects on the environment and combustion efficiency. The primary goal is to refine RDF to achieve uniform particle size and limit chlorine content. Meeting these criteria ensures smooth kiln operation and reduces maintenance needs. However, producing high-standard refined RDF requires a sophisticated process. The three most important parameters to achieve fuel quality are calorific value, low moisture content, and consistent particle size.
Good quality RDF should have a high calorific value (15-25 MJ/kg) and low concentrations of toxic components like metals, with moisture content below 20% to ensure sufficient energy is generated for clinker production. Chemically, the chlorine content in the waste should be below 1% and sulfur below 2% to prevent damage to kiln components and excessive emissions. Additionally, non-combustible substances like metals, glass, and stone, which can cause blockages, must be removed during segregation.
Vision AI is emerging as a key solution for numerous industrial challenges, moving beyond being just a buzzword, particularly in automation and analysis. In the cement industry, Vision AI can be utilized to oversee and evaluate kiln operations, enhancing sustainability efforts. By integrating AI with a plant’s advanced process control (APC), teams gain valuable data-driven insights that help boost their thermal substitution rate and make informed operational and procurement decisions in the dynamic alternative fuel markets. The vision system provides more flexibility and a contactless approach to analysis without disturbing the process flow. Let’s see how the AI vision system aids fuel analysis in each section.
Powered by AI vision technology, advanced process control can achieve automation by detecting critical parameters and adjusting processes to mitigate potential risks. The AI vision system enables users to store, analyze, and detect anomalies in real-time visual data acquired from the plant. This significantly increases the efficiency of using alternative fuels by identifying possible blockages or risks early on.
Data engineering and visual data lake creation: This is the first step in achieving advanced process control using visual AI. Real-time monitored data from the plant or cement kiln is stored and analyzed for immediate control or later evaluation to identify potential deviations. A data lake enables the integration, processing, and analysis of video data from multiple sources. It also supports data visualization and creates automated workflows for operational efficiency.
Self-cleaning mechanism: Cement manufacturing operates in a dusty environment where camera lenses can quickly become dirty, obscuring visuals. The self-cleaning mechanism uses sensor detection to identify contaminants on the lens and clean it automatically, ensuring uninterrupted monitoring.
Application-based and WhatsApp alert system: Alerting users to detected anomalies is as important as the detection itself. Modern AI vision systems connected through mobile and desktop applications can send alert notifications instantly. Additionally, WhatsApp notifications allow supervisors to receive alerts even when they are away from the plant.
As discussed earlier, RDF is a complex mixture of waste, making it challenging to accurately analyze its calorific value. AI vision systems, equipped with advanced image recognition and machine learning capabilities, can detect the proportions of various components in RDF. Unlike traditional techniques, vision systems analyze these proportions in real time without disturbing the fuel flow. Once the vision analysis system calculates the proportion of material, the AI system can estimate the calorific value accurately.
CONTREC Messtechnik - Positive Material Identification (PMI) with EDXRF
AI-powered real-time data analysis provides valuable insights into process control parameters such as temperature and gas analyzer data, which can help identify potential blockages or ring formation. Thermal analysis, assisted by AI, detects unusual thermal patterns in the kiln that may indicate ring formation. Additionally, visual data offers insights into combustion efficiency, enabling the system to predict the fuel required for optimal burning and maintain stable kiln operation.
Excess moisture in RDF leads to inefficient combustion and compromises clinker quality. High moisture content can also cause material agglomeration, resulting in uneven material flow, ring formation, or buildup on kiln walls.
AI vision systems use infrared imaging and color analysis to detect moisture levels. Infrared imaging identifies temperature variations in RDF, which correlate with moisture content, while color analysis detects darker color structures that often indicate higher moisture levels.
Ensuring consistent fuel particle size is crucial to avoiding blockages and ensuring complete combustion. AI vision systems use object detection to identify large particles, metals, and PVC materials that could disrupt kiln operations. These systems continuously monitor the particle size on the conveyor belt before the fuel reaches the kiln, allowing for corrective actions to be taken promptly.
By integrating with advanced process control (APC) systems, Vision AI can optimize kiln operations by leveraging real-time data to make immediate adjustments to the fuel feed rate. By improving combustion efficiency and reducing emissions, Vision AI contributes to a more sustainable and environmentally friendly cement production process.
Vision AI can also be used to perform volumetric analysis of materials, providing accurate measurements of volumes of different components in the fuel mix. This information can be used to optimize the fuel blend.
The adoption of RDF as a fuel for cement kilns offers significant cost savings and environmental benefits. However, the complex nature of RDF necessitates high-quality standards and advanced processes to ensure efficient combustion and minimize operational challenges. Integrating AI vision systems into cement and steel plants enhances the ability to monitor and analyze fuel quality, detect anomalies, and optimize combustion processes. This technology ultimately supports more sustainable, cost-effective, and efficient production.
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