Use Cases

Background Manufacturing plants often rely on both railcars and trucks to ship their products to distributors, retailers, or directly to customers. The choice between these transportation methods depends on various factors such as the nature of the goods, cost, distance, and delivery time requirements. Railcars are typically used for bulk shipments of heavy and large quantities of goods, such as raw materials, chemicals, and pellets, due to their capacity to transport large volumes efficiently over long distances. Conversely, trucks offer greater flexibility and are ideal for shorter distances and time-sensitive deliveries, providing door-to-door service and easier access to various destinations. This combination of transportation methods allows manufacturing plants to optimize their logistics and ensure timely and cost-effective distribution of their products.   Method #1 can be reviewed here. Although that method yields accurate results, it may not always capture events of varying du

BackgroundThis use case shows how to create a dashboard that shows an overview of the number of valve openings and the current valve state. In the past this used to be a non-automated procedure, requiring a lot of manual work to count the amount The objective of this dashboard is to streamline and automate the monitoring of valve operations, reducing manual effort and enhancing real-time visibility.Example of a valve opening tagSolutionThe solution includes the following steps:Value based search: Create a value based search to identify all the valve opening events you want to display on your dashboard. Save this search for later use. Contextualisation of Events: Select “Add as Context Item” for the identified events. Fill in the context item details such as Component (valve opening tag), Type (High Flow), and Description (Valve open). Monitor Setup: Go to the “Monitoring” tab and enable a new monitor. Use the saved valve opening search and set it to create a context item for each event

BackgroundIn batch processes, tracking key metrics like batch start and end times, cycle durations, and critical values such as maximum temperature can provide valuable insights. For instance, maintaining temperature within a certain threshold during a batch step can prevent quality issues and ensure process safety. This use case will guide you through how to document, track, and analyze batch cycle times, utilizing TrendMiner’s TrendHub and ContextHub features to streamline and automate this process.ChallengeTraditionally, batch cycle times have been recorded manually in Excel spreadsheets by board operators. This manual entry system often results in inconsistencies, especially when operators miss recording the start or end times of batches. TrendMiner provides a more reliable solution by automatically identifying and documenting batch steps and storing this information alongside tag data. By automating these records, TrendMiner reduces the reliance on Excel and increases accuracy.Sol

BackgroundThis use case shows how to create a custom visualization / chart that can be displayed on your DashHub dashboard as a tile. We can do this via MLHub. In this use case example, we will create a bar chart to track a key process metric at a facility, monthly average energy usage. Once we have added this visualization as a tile to the target dashboard, we can add other visualizations, as needed, following a similar procedure. These dashboards can then be shared with your peers. SolutionThe solution includes the following steps:Create a TrendHub view: Create a TrendHub view with all of the relevant tags. Adjust the time period on your focus chart. You will want to include all of the data that will be used to create your visualization. Save this TrendHub view.                                                                          TrendHub view with focus chart duration spanning the entire year of 2024.  Create a new MLHub notebook: Navigate to MLHub and create a new notebook. In

👉🏻 BackgroundIn a water purification system, efficient sludge transport is crucial tomaintaining optimal operations. The system employs five conveyor belts,which are dynamically activated based on the sludge load. At any giventime, a minimum of one and a maximum of four belts may be inoperation. However, the dynamic nature of this system introduceschallenges. If one of the belts malfunctions or operates inefficiently, it canlead to increased sludge accumulation, potentially disrupting the entirepurification process. To prevent this issue, a robust monitoring solution isessential to ensure that all belts function properly, regardless of the load.🕵 ChallengeMonitoring the operation of the conveyor belts is complicated by thevariability in the number of active belts. This dynamic operation makes itdifficult to determine when a belt is malfunctioning versus when it issimply under a different load condition. The challenge lies indistinguishing normal variations in performance from actual

  Background Monitoring thresholds is a common use case in the chemical industry, essential for maintaining consistent production quality and ensuring operational safety. Thresholds act as predefined limits, signaling potential issues in the process when exceeded. Failure to effectively monitor these thresholds can lead to production inefficiencies, equipment damage, and reduced product quality. Such deviations can be caused by factors like temperature fluctuations, pressure variations, or torque instability, as well as unexpected changes in raw material quality. TrendMiner provides an effective solution by enabling reliable and continuous monitoring of these thresholds. ChallengeThe challenge in monitoring thresholds lies in precisely defining and continuously tracking them. TrendMiner offers a solution by enabling users to set up monitoring through value-based searches, which can trigger automatic alerts. Additionally, the creation of context items can be activated to allow for detai

BackgroundIn one of the distillation units, one of the product temperatures is being lower than expected for around 1 year, leading to lower product recovery, as the separation is not as efficient as it should. This means a 10% recovery loss in the company, which is an important economical loss. ChallengeTroubleshoot the causes for this deterioration in performance, by detecting the influencing variables and the respective influence of each. Solution-Load all the related tags, as well as the key temperature-Run the correlations engine to find correlations against the potentially correlated variables and against all the variables of the asset-Layer 2 periods (high and low recovery) and compare statistics and trends of the potentially influencing parameters Results and value-Two process variables were identified as the main influencing factors. -It was identified that both variables weren't acting at the same time, but separately. That's why by themselves, the correlation is not to high,

 Background Fluctuating process parameters in the chemical industry present a significant challenge as they can impact the efficiency and quality of production. These variations can be caused by inaccurate measurements, unstable raw material quality, or insufficient process controls.For instance, Torque fluctuations in machinery may lead to mechanical damage, compromising operational safety. Temperature fluctuations in reactors can affect reaction kinetics, resulting in inconsistent product quality. Likewise, pressure fluctuations in pipelines or reaction vessels can affect product consistency and equipment safety. ChallengeManaging fluctuating process parameters may require setting and monitoring thresholds, tracking the range of variations, and creating context items. TrendMiner simplifies this by allowing users to define and oversee limits, measure fluctuations, and visualize all relevant data on a dashboard.  SolutionDefining upper and lower limitsIn this step, upper and lower torq

BackgroundManufacturing plants often rely on both railcars and trucks to ship their products to distributors, retailers, or directly to customers. The choice between these transportation methods depends on various factors such as the nature of the goods, cost, distance, and delivery time requirements. Railcars are typically used for bulk shipments of heavy and large quantities of goods, such as raw materials, chemicals, and pellets, due to their capacity to transport large volumes efficiently over long distances. Conversely, trucks offer greater flexibility and are ideal for shorter distances and time-sensitive deliveries, providing door-to-door service and easier access to various destinations. This combination of transportation methods allows manufacturing plants to optimize their logistics and ensure timely and cost-effective distribution of their products. ChallengeKeeping an accurate count of how many vessels have been filled can be a challenging task for manufacturing plants, par

BackgroundThe material management department needs the daily consumption of caustic to order the correct amount from suppliers and avoid shortages or excess.This is calculated from the reactor's weight sensor after each of the approximately 20 daily discharges.Before TrendMiner, we manually recorded these weights, a tedious process prone to errors, missing data, and a lack of consumption analysis. ChallengesUnderstand the normal daily raw material consumption to request the needed amount from providers and optimize inventory space. SolutionCreate a formula to create the derivative of the reactor weight only when it's negative (discharge).   Value based search to search every day in the last 6 months Add calculations with the day name and the integral of the formula (daily consumption) Export report  Results and value-A report of the last 6 months was created in 10 minutes, the same amount of time that it takes to manually get the report of 1 day. From now on, only 1 click is needed

This use case demonstrates how TrendMiner's functionalities can be leveraged to perform retrospective analyses, leading to enhanced productivity and efficiency in our power plant operations. BackgroundIn our power plant, managing fuel consumption efficiently is crucial for operational cost savings and environmental impact reduction. This use case focuses on a retrospective analysis aimed at reducing fuel consumption by adjusting PID (Proportional-Integral-Derivative) parameters within our system.ChallengeThe primary challenge was to reduce fuel consumption without compromising the performance and stability of the power plant. The desired outcome was to identify the impact of PID parameter adjustments on fuel consumption by comparing data from specific periods before and after the changes were implemented.SolutionTo address this challenge, the following steps were taken: Parameter Adjustment: The PID parameters were adjusted in our power plant system to optimize fuel consumption. Data

BackgroundThis use case is centered on finding the median value of a collection of tags. In TrendMiner, it is simple to calculate the mean or average value of a set of tags. This can be done in our formula tag builder. In some cases, however, the median value proves to be more useful of a central statistic than the mean. For example, consider a furnace where there are multiple thermocouples measuring the same point temperature. If one of these thermocouples were to malfunction and read a very high or very low value, the mean value will be skewed high or low, respectively. When outliers exist in the data, the median value proves to be a useful central statistic. ChallengeThere is no built-in function in formula tag builder for calculating the median value of a collection of tags. A simple, manual formula can be created to find the median for a collection of two or three tags; however, the manual formula grows exponentially when additional tags are added. Writing a formula to calculate t

BackgroundThis use case shows how to create a dashboard that shows the number of events per day of the week. This can be emptying/filling of a tank or number of produced batches, or any event indicated by a tag in TrendMiner. Having such an overview can help you gain better insights in your process operations.SolutionThe solution includes the following steps:Value based search: Create a value based search to identify the events you want to count on your dashboard. Based on the search results create context items as stated in step 2. Also save the search to enable a monitor based on it for future context item creation. Context Item Creation: Each time an event happens, a context item is created to record the event via the search results or the monitor. This context item captures pertinent details such as the timestamp of the event and the day of the week as a keyword. Keyword Tagging: The day of the week is tagged as a keyword for every context item, facilitating easy categorization and

BackgroundIn our batch process, ensuring optimal operating conditions is critical for maintaining production, product quality and worker safety. Monitoring various process parameters such as low average temperature, constant pressure, high end- concentration and low energy consumption in real-time is essential to maintain consistency and quality across batches. ChallengeOur challenge was to implement a comprehensive system for monitoring operating conditions in a batch process with 7 regularly changing recipes. Over the past month, we have experienced problems with parameters deviating from optimal process conditions, resulting in poor product quality. We needed a solution that would accurately monitor the trend profiles at each step of the process and alert operators when these deviations occurred. The alert should provide actionable insight for operators and supervisors to optimize production processes and minimize product loss. Solution1. Visualizing the trendsA list of tags was add