Functional Tips & Tricks

You may have experienced that when working with stepped tags in formulas, your output is not quite what you were expecting. As an easy example, let's look at a batch process where we have a a discrete tag with the quality (top), and a digital tag with the product name (middle). Suppose now we want to create a tag that only shows the quality when the product is Alpha, you might think to implement it as in the screenshot below. However, as you can see, the result (bottom tag) is not quite what you would be expecting:  So why we do we get this sawtooth pattern? The first thing to understand is that our resulting formula tag only gets calculated where one of our input tags has a datapoint. Where there is a datapoint for any of the input tags, values for the other input tags are obtained through interpolation. The second factor

 In the chemical industry, monitoring specific events that occur within a defined time frame - such as under X minutes - is crucial for ensuring safety, maintaining product quality, and optimizing processes. While such short-duration events might be fleeting, their impact can be significant, and timely detection is essential to prevent disruptions or inefficiencies. In this article, I will demonstrate how to monitor short-duration events in TrendMiner effectively, enabling proactive responses to these critical occurrences.Why monitor short-duration events?Short-duration events can provide key insights into process dynamics, equipment performance, and potential risks. By monitoring and addressing these brief occurrences, you can enhance operations and safeguard compliance. Here are some practical examples:Environmental compliance and safety

TrendMiner offers three main methods for calculating and tracking consumption or production over time, depending on your goals. You can use the Statistics Table, Calculations on Search Results, or set up a Consumption/Production Tag for continuous reporting. 1. Using the Statistics TableCalculating consumption or production for a given period is a simple one-step process, assuming there is already a tag representing the flow, rate, ... that is to be quantified. If this is not the case, such a tag can be created as a rate or through a formula. Once such a tag is in place you can navigate to the relevant period and open the statistics table. Here all the corresponding values can be read immediately. Often the total amount for the selected period is of interest, which can be read from the integral value, although the aver

It can occur that we wish to know the flowrate of a certain material stream, but that the only measurement we have available is the weight or level of an intermediary vessel. This is especially applicable to the processing of solids, where measuring a flow directly is challenging. In this example, let's then work with the concept of deriving a mass flow from a total weight.In essence, the mass flow is nothing more that then change (delta) of the weight of our vessel, multiplied by a constant value (to obtain the result in the desired units). As a starting point, we could for example take the weight in kg of our vessel 5 minutes ago, subtract the current weight, divided the result by 300 (the number of seconds in 5 minutes), to obtain a mass flow in kg/s:Note that the choice of the interval duration (here 5m) should depend on how n

 In industrial data analysis, preprocessing sensor data is crucial for accurate results, especially when dealing with sudden, abnormal spikes. These spikes can distort your analysis and lead to incorrect conclusions if not addressed properly. In this article, I will show you how to eliminate spikes from your sensor data using four different methods in TrendMiner, ensuring that your analysis is reliable and accurate.Examples of spikes in sensor dataSpikes in sensor data can occur due to various reasons such as sensor malfunctions, temporary communication errors, or unexpected events in the process. For instance, a sudden spike in temperature readings from a reactor might be caused by a faulty sensor rather than an actual temperature increase. Similarly, pressure spikes in a pipeline could be due to momentary errors in measurement. Removing these spikes from the da

Background So, you've found yourself wanting to historicize something you've observed in TrendHub. As the process engineer for the past two years, you've noticed a clear pattern between unit swings and recurring filter change on a feed pump. Now, you need to compile these findings into a list and annotate them on the trends. That is the only way you can convince everyone else that this is indeed the root cause of the swings and not just some random incident. Although you've briefly heard of ContextHub, once you reach this screen, you find yourself wondering, "What is a component, and what do you mean by Type???"  Component and Type are two parameters that must be assigned to create a context item, or a block of information linked to a data point or a period of data associated with an event ident

 In industrial data analysis, it is often necessary to formulate complex conditions to achieve precise search results. However, it is not possible by default to combine AND and OR conditions within a value-based search in TrendMiner. TrendMiner allows you to combine multiple conditions in a value-based search, but you can only combine all conditions with either AND or all with OR—not both simultaneously. Here, I will show you how to effectively combine AND and OR conditions in a value-based search.Examples of combining AND and OR conditionsA typical example would be searching for production data where multiple products (e.g., ALPHA and BETA) are analyzed under specific temperature conditions. Other examples include identifying production phases where certain machines are either in "Standby" or "Maintenance" mode, but only if energy consumption is below a defined threshold. Another example could be analyzing phases where specific chemicals are ei

Sometimes, for better visualization and monitoring, it is helpful to represent the durations of certain important events in the plant within a variable. This is possible in TrendMiner by following a couple of steps.First, it is necessary to create a slope tag, from 0 to 1, that connects the start and end points of the event.This can be done with a conditional formula and time changes. For example, an event from the moment a batch starts until the “Phase 3” starts: It is important that the obtained variable consists of constant slopes from 0 to 1 and from 1 to 0. We do not want it to remain constant at values of 0 or 1 for long periods, as this could affect the second step of the

BackgroundAs a process engineer, understanding the relationship between two variables is crucial and often necessary. For example, how does tower pressure impact overhead rates? How does temperature affect conversion? Operations are constantly making adjustments, and process engineers need to grasp the impact of various levers or handles on final product specifications, operating envelopes, and process safety. TrendMiner enables users to quickly analyze relationships between tags or variables, both visually and quantitatively.Scatterplot of natural gas usage vs carbon emissions.Steps to Analyze Relationships in TrendMiner:Step 1: Add the tags you are interested in analyzing to the active tag list for bivariate relationships.Step 2: Switch to the Scatterpl

In our latest release, 2024R2, we’ve enhanced the Scatterplot display in TrendHub to visualize all layers of a TrendHub view, not just the base layer. This new feature enables you to compare relationships between different tags across multiple layers, making it a powerful tool for troubleshooting, root cause analysis, performance grading, and more. In this article, we'll walk you through how to use this new feature to its full potential.Step 1: Adding Different LayersThere are several ways to add layers in TrendMiner. In our example, we used value-based searches to identify high- and low-quality batches and added the three highest quality and three lowest quality batches as layers.Overview of all the layersStep 2: Switching to Scatterplot ViewBy changing the plot visualization to a scatte

Creating clear and effective graphs is a vital skill for anyone involved in data presentation. In process engineering, the ability to visually convey complex information can significantly enhance the impact of your work. However, not all graphs are created equal. Poorly designed graphs can mislead, confuse, and obscure the very data they aim to highlight. This functional tip outlines essential best practices for making enhanced TrendHub views that are both informative and visually engaging. By following these guidelines, you can ensure your graphs not only accurately represent your data but also resonate with your audience, facilitating better understanding and decision-making. Keep It Simple: Avoid clutter by minimizing gridlines unless they are necessary.     Try to avoid displaying exce

 In industrial data analysis, precisely examining short events is crucial for optimizing processes and preventing disturbances. Short events, which are shorter than the index resolution, present a unique challenge as they can easily be overlooked. Here, I will show you how short events can still be analyzed in TrendMiner using a value-based search.Examples of short eventsExamples of short events include sudden pressure spikes in pipeline systems, which may indicate valve malfunctions or blockages, temporary temperature fluctuations in reactors, which can point to unexpected chemical reactions, and brief electrical spikes in systems, which may signal short circuits or overloads. Analyzing these events accurately allows for taking preventive measures and improving process stability and system safety. 

Often, we want to analyze or report on key KPIs, such as consumption, production, average temperatures, or maximum values of critical variables. However, these data need to be analyzed separately based on the recipe they correspond to.In TrendMiner, it is possible to unify all workflows so that reports for multiple recipes can be obtained with a single process. This is due to the ability to include the “constant” condition in the Value Based Search and to add string variable calculations. Here’s how it is done:Prepare the View: Load the tag indicating the recipe name and the rest of the necessary tags to create the report. Perform the Search: Go to the search menu and start a value-based search. The condition will be: Recipe name tag CONSTANT. The results obtained will correspond

Background:TrendMiner's "Event Analytics" is typically used for batch processes, but it can also optimize continuous processes. By breaking down the data into manageable hourly intervals, this approach provides detailed monitoring and identifies optimal conditions for enhanced efficiency and productivity.Steps: Preparation: Add the most important process parameters. Include the pre-installed TM_day_ tag. Data Splitting: Use the value-based search feature to segment data. Set the condition to TM_day_ constant. Ensure a minimum duration of 59 minutes. Adding Calculations: Add calculations on the search result. Example calculations: Average Temperature, Integral Cooling Water, Minimum Pressure.

BackgroundOften times, we find ourselves wanting to know when a certain tag changes state. For example, if we have a batch process, I’d like to know when I transition from one phase to another in the process. Or for a multi-product line, I would like to know when we switch from manufacturing product A to product B. Not only would we like to know when these state changes occur, but also it would be useful to analyze the data per state or segment. Consider the batch process example again - it would be nice to know the duration of each phase. Or even, what is the maximum temperature at each phase of the batch process?In TrendMiner, we can quickly answer these questions by conducting a constant Value Based Search (VBS). When selecting constant in a VBS, we ask TrendMiner to identify every change of state or level. I say level too because constant VBS can apply to analog tags in addition to string and discrete tags. Below I will detail the steps to create a

We all love the monitor feature in TrendMiner, however sometimes it can also be annoying when the monitor is triggered too many times and creates an email spam. In this article we will discuss how you can prevent an endless stream of TrendMiner monitor alert (emails) and just get an alert when a real event occurs.1. Use search results as indication for number of alertsA TrendMiner search is often the backbone of a monitor and thus we can use the number of search results as indication to the amount of monitor alerts we will get within the search period. For example, if we perform a search with a context bar period of 6 months and get 15 results, then we know that when we apply a monitor with this search we will get 15 monitor alerts within 6 months.2. Finetune your value based searchWhen you use a value based search the first thing you can tune to optimize your monitor is the minimal duration setting of your value base. In this example we want to know

In this article, we present 4 of our least used but valuable features which you can use to your advantage. We also show some concrete examples. 1 - Operating Area Search Empowers users to intuitively identify optimal and suboptimal operating conditions through a visual interface.  Helps users detect process irregularities early by analyzing correlations between parameters.