Predictive Maintenance and Pulp & Paper Industry
Pulp and paper production is a vital industry that produces a wide range of products, from paper and cardboard to tissue and pulp. However, the production process is complex, energy-intensive, and requires a large number of machines and equipment to operate. As a result, ensuring that these machines and equipment are running smoothly and efficiently is crucial to the success of the industry. This is where predictive maintenance comes in. In this blog post, the main focus will be on the relationship between predictive maintenance and the pulp and paper industry.
Predictive maintenance is a proactive approach that involves collecting data and analyzing it to predict when equipment is likely to fail. This allows maintenance teams to take corrective action before a failure occurs, reducing downtime and increasing the overall efficiency of the production process. In the pulp and paper industry, predictive maintenance can be used to monitor and maintain a wide range of equipment. The machinery used in the pulp and paper industry includes pulping equipment, such as kraft pulping systems and mechanical pulping systems, as well as chemical recovery systems and bleaching systems. Other important machinery includes papermaking machines, such as fourdrinier machines and cylinder machines, and finishing equipment, such as calenders, rewinders, and slitters. Additionally, various types of pumps, valves, and conveyors are used to transport and process the materials throughout the mill. The industry also has a wide range of supporting equipment like boilers, turbines, generators, and air compressors. Depending on the layout, production strategy and focused products of a facility, all of the machinery mentioned above has the possibility to be crucial. In order to maintain product quality and efficiency of production predictive maintenance applications are a must for the pulp and paper industry.
One of the most effective predictive maintenance techniques for the pulp and paper industry is vibration analysis. Vibration analysis involves measuring the vibration of equipment and analyzing the data to detect abnormal patterns that indicate a potential failure. This can be done using specialized sensors and software, which can detect issues such as unbalance, bearing failure, and gear wear. By detecting these issues early, maintenance teams can take corrective action before a failure occurs, preventing costly downtime and ensuring that production lines are running smoothly. Main focus of vibration analysis is rotational machinery. Vibration data gathered from critical components of an equipment such as bearings of electric motors and gearboxes can shed light on many possible malfunctions of the machinery long before large-scale damage occurs. Predictive maintenance can also be done with the help of IoT-based data collection and analysis. This allows the monitoring of a large number of data points on the equipment and can provide real-time data to the maintenance team. This can help reliability agents to analyze data trends and operational tendencies of a machinery and address potential issues before they escalate.
Sustainability is one of the key concepts the pulp and paper industry is focusing on. Predictive maintenance and sustainability are closely related since predictive maintenance applications aim to increase overall efficiency of a system. Sustainability is the practice of ensuring that resources are used responsibly and in a way that does not harm the environment or future generations. In the pulp and paper industry, this can include measures such as reducing energy consumption, minimizing waste, and using sustainable raw materials. By implementing predictive maintenance, the pulp and paper mills can reduce energy consumption and waste by identifying and correcting inefficiencies before they lead to breakdowns. Additionally, regular maintenance and repair can prolong the life of equipment and reduce the need for replacements, which can help to reduce the environmental impact of the mill. Furthermore, by detecting equipment failures early, the mills can avoid unexpected downtime, which can result in wasted raw materials and energy. Predictive maintenance also allows mills to plan maintenance activities in a more effective way, which can reduce the environmental impact of maintenance activities by minimizing the amount of chemicals used and reducing the amount of waste generated during maintenance.
In conclusion, the pulp and paper industry relies heavily on the performance and reliability of its equipment. Predictive maintenance is a crucial tool for ensuring that this equipment is running smoothly and efficiently, reducing downtime and increasing productivity. Techniques such as vibration analysis combined with IoT-based data collection and analysis, can provide valuable insights into the health of equipment, allowing maintenance teams to take proactive, corrective actions before a failure occurs. Implementing predictive maintenance can help the industry to become more sustainable and cost-effective.
Sensemore Maintenance Bot
Elevate your maintenance with Sensemore’s AI-powered predictive bot for increased uptime and cost savings.
In the world of industrial maintenance, keeping equipment running smoothly and efficiently is crucial for the success of any organization. But with the increasing complexity of modern machinery, it can be difficult to keep track of all the potential problems that could occur. This is where artificial intelligence (AI) comes in. Artificial intelligence has the potential to revolutionize many aspects of our lives, and one area where it is already starting to make a significant impact is in the realm of predictive maintenance.
Predictive maintenance involves using data and analytics to predict when equipment is likely to fail, so that preventive measures can be taken to avoid downtime and costly repairs. We have already discussed the history of maintenance and answered how it has evolved to be more proactive in our other blog History and Future of Maintenance Culture – Sensemore. On the other hand, today’s trend is BOTs, to be more specific AI BOTs.
An AI-powered bot, is actually a computer program that uses artificial intelligence technologies to perform specific tasks or mimic human behaviour. These tasks can include things like responding to user input, analyzing data, and making decisions. AI bots can be integrated into various applications and even today their capability is beyond human-written programs. If you do not think you have not encountered one of them, you probably have since even voice assistants in our mobile phones are also AI bots. These bots are able to generate images based on your text description, some help for coding and the most popular of all is chatGPT after its launch in December 2022. Even though they are very new in the field, they are very popular on the internet.
Going back to the original topic, the industry lacks bots when it comes to predictive maintenance. It is obvious that offline and online analysis tools provide graphical interfaces and statistical and machine learning techniques to analyze the data and make predictions. However, the outputs sometimes remain highly technical and academical for those working in the field. In order to overcome these issues, Sensemore provides an end-to-end solution from placing Sensemore sensors on your machine to its cloud platform and presents artificial intelligence analysis on collected data.
Fig.1 Sensemore Maintenance Bot Notifications
But Sensemore is more than just a predictive maintenance tool. It is also able to perform many of the tasks traditionally carried out by human maintenance workers, such as identifying and diagnosing problems, suggesting repair options, and even scheduling maintenance activities. Additionally, Sensemore’s reliability engineers communicate directly with the user to assist them about their equipments, reports and maintenance plans. This helps to reduce the workload of maintenance teams, freeing them up to focus on more complex tasks and enabling them to be more productive and efficient. For diagnose and repair options our portal sends user friendly maintenance reports that provide the root-cause of the problem. Now with the newly developed Sensemore maintenance bot, predictive maintenance is getting more effortless and uncomplicated.
Sensemore’s bot uses developed-in-house predictive maintenance algorithms such as machine mode analysis, trend type detector, faulty detector or trend predictor. This means that our bot is specifically tailored to the needs of maintenance teams and is optimized for tasks such as analyzing sensor data and predicting equipment failures almost real-time.
For example, if a piece of equipment is operating outside of its normal parameters, a bot can be configured to send a notification to a maintenance team, alerting them to the issue and providing relevant details such as the location of the machine and potential causes of the problem. In Fig.1 the bot provides notifications for the registered customers and quick look-up analysis. If further investigation is required, it directs to Sensemore cloud platform for more sophisticated tools, analysis and reports on the measurement point.
Sensemore maintenance bot is now in beta version and only available in Telegram which is a popular messaging app. Once the bot is integrated and configured, users can interact with it through Telegram’s chat interface, just as they would with any other Telegram bot. Some of the pre-defined commands can be seen in Fig.2 which helps quick reviews over mobile phones. With the help of messaging app technologies, Sensemore maintenance bot can even be send notifications over smart watches which encourages to take actions only when necessary.
Fig.2 Sensemore Maintenance Bot Available Commands
In addition to providing alerts, Sensemore bot can also be used to provide information and advice to operators and maintenance teams. For instance, the bot could be programmed to provide detailed instructions on how to perform a particular maintenance task, or to provide information about the performance and health of a particular machine. Overall, the use of bots in smart machine health platforms can greatly improve the efficiency and effectiveness of these systems. By providing real-time information and alerts, Sensemore maintenance bot can help to reduce downtime and prevent potential problems from escalating into major issues. As a result, companies that use smart machine health platforms with bots are likely to experience improved productivity and profitability.
Iron & Steel Industry and Predictive Maintenance Applications
The iron and steel industry is one of the most important fields of production. Its products are key components of many of the concepts that our lives revolve around, such as transportation, construction, infrastructure, technological devices etc. It is a major contributor to economic growth with 2.5 trillion dollar revenue and employment, as it provides 6 million direct and 40 million indirect job opportunities. Also Iron and Steel industry is a significant contributor to energy consumption and carbon emissions globally. Steel production is energy-intensive, and the majority of energy used in the industry comes from fossil fuels, such as coal, natural gas, and oil. The use of fossil fuels in the production of steel results in the release of greenhouse gasses, such as carbon dioxide, into the atmosphere, casuing to climate change. In addition, the energy used to operate the machinery and equipment in the industry also contributes to energy consumption and carbon emissions. As the industry with the highest energy consumption and 7 to 9 percent of the world’s carbon emission (2.6 Gt CO2e, highest among the heavy industries), the iron and steel industry demands innovation and attention from humankind. As a result, technologies focusing on efficiency, reliability, safety and sustainability are welcomed by the iron and steel industry. In this blog post the main focus will be on the Iron and Steel Industry and the innovative impact predictive maintenance applications can have on the industry.
The iron and steel industry relies on a wide range of machinery to produce various steel products. This machinery includes: Furnaces that are used to melt and refine raw materials, such as iron ore, scrap steel, and limestone, into steel, Rolling Mills that are used to shape the steel into different forms, such as sheets, plates, bars, and rods, Casting Machines that are used to cast the molten steel into different shapes, such as billets, blooms, slabs, and rounds, Air Blowers that are used to ventilate and circulate air, blast cleaning, drying and cooling, Pumps that are used to supply water, scrap metal handling, slag transportation, etc. In addition to these core machines, the industry also uses cranes and hoists to move materials around the facility, conveyor belts to transport materials, and specialized equipment for cleaning, coating, and finishing the steel products. Among this machinery, certain machines that work with metal at high temperatures, such as furnaces, casting machines, and rolling mills, are particularly critical. This is because a failure in these machines can result in significant energy losses, as they consume large amounts of energy in their operation. In addition, a malfunction in these machines can disrupt the entire production process, leading to downtime and lost profits. As a result, it is important to ensure that these machines are well-maintained and operate efficiently to minimize the risk of failure and energy losses.
Predictive maintenance is a proactive approach to maintaining equipment that involves using data and analytics to predict when equipment is likely to fail or require maintenance. By analyzing usage data and identifying patterns that suggest maintenance is needed, industry professionals can schedule maintenance at a time that is most convenient and cost-effective. This approach helps to optimize equipment usage and can result in significant cost savings by reducing the overall cost of maintenance. Additionally, by eliminating unplanned downtimes, iron and steel facilities can prevent energy loss due to the loss of heat that occurs when processes are interrupted. This can help to increase productivity and improve overall operational efficiency.
Predictive maintenance applications can be implemented through the use of sensors and creating a condition monitoring system. These sensors can be vibration, temperature, oil, current, voltage etc. sensors. These condition monitoring techniques are implemented on equipment and machinery to track performance and detect any potential issues before they occur. For continuous processes in the iron and steel industry and the high number of critical equipment creating a condition monitoring system based on vibration analysis is the most viable choice both due to the availability of the technology and the ability to forecast possible malfunctions on rotating machinery. Another application of predictive maintenance in the iron industry is through the use of machine learning algorithms. Due to the constant nature of the processes and the ability to deploy stationary sensors enables agents of reliability to achieve a large enough data set, “Big Data”, feed machine learning algorithms. These algorithms can analyze data from sensors and other sources to identify patterns and predict when maintenance is needed. Sophisticated ML algorithms such as ones created by Sensemore can even predict the root cause of a malfunction alongside the estimated time of occurrence through equipment specific learning processes and high analysis capabilities. This allows iron industry professionals to schedule maintenance at a time that is most convenient, rather than waiting for a breakdown to occur.
In conclusion, the iron and steel industry is a vital contributor to the global economy and a major consumer of energy and carbon emissions. Predictive maintenance technologies, such as the use of sensors and machine learning algorithms, can help to improve the efficiency, reliability, safety, and sustainability of the industry by predicting when maintenance is needed and optimizing equipment usage. These technologies can help to reduce energy consumption and carbon emissions, as well as improve the overall efficiency of the industry. Implementing predictive maintenance in the iron and steel industry can also help to reduce downtime, improve product quality, and increase profits. Overall, the use of predictive maintenance in the iron and steel industry can have significant benefits for the industry, the environment, and society as a whole.
References:
- Kim, Jinsoo, et al. “Decarbonizing the Iron and Steel Industry: A Systematic Review of Sociotechnical Systems, Technological Innovations, and Policy Options.” Energy Research & Social Science, vol. 89, 2022, p. 102565., https://doi.org/10.1016/j.erss.2022.102565.