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Pump Health Monitoring AI
Inability to detect and anticipate pump performance issues can result in prolonged shutdowns, revenue loss, and potential environmental or safety hazards.
Batch Tracking and Cycle Time Analysis AI
Reducing cycle time in batch manufacturing is challenging due to the complexity of defining and analyzing process phases to identify variations and idle times between batches. Additionally, pinpointing areas for process and capital improvements requires detailed understanding and analysis. Effective batch tracking and cycle time analysis are essential to uncover inefficiencies, minimize idle times...
Filter Membrane Predictive Maintenance AI
Ineffective Clean-In-Place (CIP) procedures or fouling of filter membranes can lead to various operational challenges, including increased cycle times, lost yield, or poor product quality. Additionally, detecting and modeling long-term deterioration in filter membrane performance poses significant challengess. This use case focuses on developing predictive maintenance strategies for filter membran...
Distillation Column Efficiency AI
The Golden Batch in distillery manufacturing represents the ideal production outcome, aligning critical parameters like fermentation, distillation, and maturation to ensure top-quality, consistent spirits. It serves as a benchmark for optimal flavor, alcohol yield, and process efficiency. Modern tools like advanced process control, real-time analytics, and machine learning help distilleries monito...
Knowledge Accelerators for Agentic AI – Revolutionizing AI for the Chemical, Process & Oil & Gas Industries.
Powered by Amazon Bedrock & Neptune The chemical and process industries are undergoing a transformation driven by artificial intelligence (AI). Traditional AI and machine learning models have supported industrial operations, but their lack of contextual awareness and limited decision-making capabilities have restricted their impact.
Distillation Column Efficiency
The Golden Batch in distillery manufacturing represents the ideal production outcome, aligning critical parameters like fermentation, distillation, and maturation to ensure top-quality, consistent spirits. It serves as a benchmark for optimal flavor, alcohol yield, and process efficiency. Modern tools like advanced process control, real-time analytics, and machine learning help distilleries monito...
Steam Consumption Optimization
In many seasoned process manufacturing plants, the growing production capacity often leads to a mismatch with the available steam production capacity, creating significant inefficiencies. Additionally, the reliance on steam for both production processes and electricity generation can compound the issue, resulting in increased operational costs and energy waste.
Fouling Factor Prediction
Fouling in heat exchangers is a critical challenge in industries where fluid flows through tubes and heat transfer surfaces, leading to the deposition of dissolved impurities. As these fluids reach saturation, the buildup of scale and other materials reduces heat transfer efficiency and can create sites for corrosion. This not only compromises the performance of the equipment but also accelerates ...
Statistical Process Control
In the distillation industry, precise control over batch operations is essential to ensure consistent product quality. However, challenges such as improper reflux control can disrupt the separation process, leading to variations in cut-compositions and lack of clarity between first and main cuts. These inconsistencies can have a significant impact on product purity and process stability.
Cooling Tower Health Monitoring
Cooling towers play a pivotal role in maintaining the efficiency of heat exchanger networks in distillation processes. However, their performance is highly variable, influenced by daily operational fluctuations and seasonal changes, which can obscure underlying inefficiencies and potential issues. These challenges, if left unaddressed, can lead to increased water losses, higher energy consumption,...
Multi-Effective Evaporator (Steam)
In distillery manufacturing, maintaining optimal performance of multi-effect evaporators is crucial for maximizing efficiency and minimizing downtime. However, the changing characteristics of feed can lead to fouling, which in turn reduces heat transfer efficiency and may result in unanticipated failures. As heat transfer becomes inefficient, the system's ability to perform effectively diminishes,...
Plant Wide Dashboard
In distillery manufacturing, ensuring operational efficiency and product consistency demands real-time monitoring of key metrics throughout the production process. A Plant-Wide Dashboard provides a centralized digital interface that delivers critical insights into equipment performance, energy consumption, and batch quality. These dashboards are vital for identifying inefficiencies and potential i...
Overall Equipment Effectiveness & Availability (OEE) Reporting
In distillery manufacturing, effective OEE reporting is essential for understanding equipment performance and identifying process inefficiencies. OEE provides a comprehensive view of equipment availability, performance, and quality, helping manufacturers pinpoint areas where improvements can be made. However, standardizing and scaling OEE analysis across diverse assets can be challenging, especial...
Overall Equipment Effectiveness & Availability (OEE/A)
In the distillery industry, achieving operational excellence hinges on the performance and reliability of production equipment. Overall Equipment Effectiveness (OEE) and Availability are key metrics that provide a comprehensive view of equipment efficiency, highlighting areas such as downtime, speed loss, and quality issues. While OEE combines availability, performance, and quality to measure over...
Golden Batch for Distillery Industry
In distillery manufacturing, the Golden Batch concept symbolizes the ideal production outcome, where critical parameters like fermentation and distillation are flawlessly aligned to deliver consistent, high-quality results. Achieving this benchmark is crucial for maximizing product yield, ensuring process stability, and maintaining production efficiency. Variations in batch processes, including fl...
Leveraging Knowledge Graphs to Add Context to Your Data
Introduction What are knowledge graphs? Have you ever come across this technology? Before we understand the core meaning of this, it's important to understand it conceptually.
Why is an Industrial Data Strategy Crucial for Overcoming Scaling Challenges in Digital Transformation?
The industrial landscape is undergoing a significant shift. As digital transformation takes hold, data is emerging as a critical driver of efficiency, innovation, and competitiveness. However, the journey towards harnessing the full potential of industrial data is fraught with challenges, particularly when it comes to scaling operations across diverse environments. A robust industrial data strateg...
Optimizing Industrial Operations with Unified Industrial Edge Computing and Cloud Solutions
Digital transformation involves integrating digital technology into all business areas. This process is increasingly facilitated by the combined solution of industrial edge computing and cloud solutions. Edge computing solutions connect with sensors, edge devices as well as control systems through edge devices such as IoT gateways, industrial PCs, and specialized edge servers. These devices collec...
Real-time Optimization in Cement Manufacturing Plant
With ever-growing demand for infrastructure and building materials, concrete manufacturers have been expanding its production facilities more than ever. With every ton of expansion, there is a challenge observed in optimization of resources, fuel and inventory. Cement manufacturers hold the processes which are one of the most energy intensive and emission generating plants. Being one of the early ...
Converting knowledge into Insights – Generative AI
Generative AI, LLMs – Hearing a lot about It these days. Let us try to uncover what it is? Simply put – It’s an AI algorithm which has human-like thinking abilities, like the way humans can interpret text and number. This interpretation not only includes analysis, and summarization of the data but also generative capabilities, where the model can next line of actions. Generative models have gained...
Deep Learning-based Modeling Strategies for Cement Manufacturing
Modeling the manufacturing operations has always been a challenging task due to several factors leading visibility in real time and lack of critical data sometimes. Cement manufacturing process has been holding some of the complex operations including fossil fuels, alternative fuels, mix of feed and extensively high temperature to melt the feed for effective reactions to take place. This industry ...
Make Up Water Feed Optimization in Cooling Tower Operations
Unlocking efficiency in cooling tower operations entails understanding the interplay between temperature signals and equipment power. This analysis delves into identifying which temperature signals significantly impact power consumption, paving the way for predictive modeling to optimize compressor power.
Distillation Column Efficiency
Distillation columns are essential unit operations in the chemical process industry, responsible for separating mixtures into their individual components. However, these columns can operate at less than optimal efficiency, leading to increased energy consumption, reduced production rates, and higher operating costs. Artificial intelligence (AI) and machine learning (ML) offer promising techniques ...
Predictive Maintenance of Lyophilizers
Predictive maintenance for a lyophilizer, also known as a freeze-dryer, focuses on the proactive identification and mitigation of potential equipment failures before they occur. This approach utilizes various data-driven techniques and monitoring tools to track the condition of the lyophilizer’s critical components such as vacuum pumps, condensers, and temperature sensors.
Batch Alarm Analysis & Interpretation
Batch alarm analysis and interpretation involves the systematic review and evaluation of alarm logs generated during batch processes in industrial settings. This process is crucial for identifying, categorizing, and analyzing patterns of alarms that may indicate operational inefficiencies, safety issues, or equipment malfunctions. By employing statistical and AI techniques, analysts can prioritize...
Soft - Sensor for Yield Prediction
In the realm of industrial manufacturing, precision and efficiency are paramount. Yet, the challenge often lies in the delayed feedback loop between process inputs and batch yield, leading to suboptimal resource utilization and costly outcomes. Traditional methods, reliant on post-facto lab analyses, fall short in providing timely insights for proactive adjustments.
Cycle Time Optimization
Defining and analyzing historical batches using traditional analytics topls is extremely difficult, particularly due to the complex dynamics involved in batch processes.Tracking various activities within a batch poses significant, challenges, making it hard to identify inefficiencies and areas for improvement. However, dynamic dashboards provide a powerful solution, enabling near real-time monitor...
Heat Exchangers Performance Monitoring
Predicting the end-of-cycle (EOC) for a heat exchanger due to fouling is a persistent challenge for refineries. By proactively forecasting when a heat exchanger requires cleaning, refineries can implement risk-based maintenance planning, optimizing processing rates, and reducing operating and maintenance costs. Historically, engineers had to manually combine data entries in spreadsheets, spending ...
Predict Conveyer Belt Tensioning Date
In industrial operations, the gradual decline in conveyor belt tension poses a persistent challenge, often resulting in tracking issues and frequent belt failures. With a fleet of 100 belts, such failures can disrupt production schedules, leading to substantial costs and operational downtime.
Order fill projection
Predicting the end-of-cycle (EOC) for a heat exchanger due to fouling is a persistent challenge for refineries. By proactively forecasting when a heat exchanger requires cleaning, refineries can implement risk-based maintenance planning, optimizing processing rates, and reducing operating and maintenance costs. Historically, engineers had to manually combine data entries in spreadsheets, spending ...
CO & Nox Prediction for Boiler - Energy Sustainability
Effective prediction of NOx and CO emissions from boilers is crucial for regulatory compliance and environmental protection. Key process parameters (KPIs) correlated with emission levels include combustion temperature, fuel composition, air-to-fuel ratio, and flue gas recirculation rates. By analyzing both historic and real-time data, it is possible to predict NOx and CO emissions accurately. This...
Cooling Tower Health Monitoring
Unlocking efficiency in cooling tower operations entails understanding the interplay between temperature signals and equipment power. This analysis delves into identifying which temperature signals significantly impact power consumption, paving the way for predictive modeling to optimize compressor power.
Energy Yield Prediction for Steam Turbine Operations
Energy yield prediction for steam turbine operations is essential to overcoming the challenges of ensuring consistent energy production while minimizing steam losses. The fluctuating load and varying demand make it difficult to implement timely and appropriate control changes, often leading to inefficiencies and increased operational costs.
Strategizing your Predictive Maintenance Journey
With the increasing adoption of Digital Transformation and Industry 4.0, it's nearly impossible to talk about it without considering Predictive Maintenance. Be it Oil & Gas, Chemicals, Metals-Mining, renewables, or the Bio-pharma industry - everyone starts the journey with the implementation of Predictive Maintenance. Being an essential part of Digital transformation, it can be a simple plug-a...
Integrated Sustainability Opportunities in the Metals & Mining Industry
The metals and mining industry stands as one of the world’s most energy-intensive sectors accountable for significant emissions and environmental impact.
Chromatography and Transition Analysis
Chromatography and Transition Analysis play a pivotal role in various industries, particularly in the purification of substances and the separation of complex mixtures. However, ensuring the reliability and effectiveness of chromatography columns presents ongoing challenges. Over time, column substrates degrade, necessitating periodic refreshment to maintain optimal performance. Despite the signif...
Aggregate and Analyze Alarms
Aggregate and Analyze Alarms is a critical process in industrial settings aimed at enhancing operational efficiency and equipment reliability. This process involves consolidating alarm data from various sources to identify patterns and trends, facilitating engineers' analysis and troubleshooting efforts. By uncovering relationships between alarms, operators can gain deeper insights into underlying...
Continuous Process Verification
Continuous Process Verification is a cornerstone of quality assurance in manufacturing, ensuring that processes consistently meet predefined specifications. However, maintaining effective monitoring in the face of product changes, instrument calibration drift, and process upsets poses significant challenges. Additionally, establishing universal rules for Critical Process Parameters (CPPs) and Stat...
Shift Handover Report
The Shift Handover Report serves as a vital communication tool to ensure the seamless transition of operations between shifts in a manufacturing environment. Its primary objective is to facilitate the maintenance of continuous, safe, and compliant operations by effectively transferring critical process information from outgoing to incoming operators. By providing a structured framework for conveyi...
Overall Equipment Effectiveness (OEE) Reporting
Overall Equipment Effectiveness (OEE) reporting is essential for identifying process bottlenecks and maximizing production efficiency across a manufacturing site. By providing a comprehensive understanding of equipment performance, OEE reporting helps streamline operations and improve productivity. However, standardizing and scaling OEE analysis across diverse assets ensures consistent performance...
Identification, Categorization, & Reporting of Performance Losses
Manufacturing companies must systematically track, categorize, and report performance losses to identify inefficiencies and justify improvement projects. This process enables the identification of underperforming elements within the production line and provides a basis for targeted enhancements. By doing so, companies can streamline operations, enhance productivity, and allocate resources more eff...
Asset Utilization (OEE) Monitoring
Asset Utilization (OEE) Monitoring aims to analyze the performance of batch processes by identifying time spent in various phases. This analysis helps reduce unproductive process time, such as during cleaning and maintenance, and highlights differences in manual re-cleaning events between shifts. By quantifying opportunities to reduce waiting times, OEE monitoring supports improved efficiency and ...
Batch Quality Prediction
Batch quality prediction is crucial for optimizing manufacturing processes and ensuring efficient resource utilization. By accurately predicting batch quality, manufacturers can adjust process inputs in real-time, improving yield and reducing waste. This proactive approach helps minimize energy consumption and raw material usage, leading to more efficient and cost-effective production.
Accelerate Biopharmaceutical Scale-Up
Accelerating the scale-up of biopharmaceutical production is essential for bringing new therapies to market more quickly and cost-effectively. Predicting cell growth accurately at scale, based on data from laboratory and pilot studies, is a complex task. This complexity is compounded by the need to integrate data from various sources. Streamlining this scale-up process can significantly reduce dev...
Batch Tracking and Cycle Time Analysis
Reducing cycle time in batch manufacturing is challenging due to the complexity of defining and analyzing process phases to identify variations and idle times between batches. Additionally, pinpointing areas for process and capital improvements requires detailed understanding and analysis. Effective batch tracking and cycle time analysis are essential to uncover inefficiencies, minimize idle times...
Pump Health Monitoring
Inability to detect and anticipate pump performance issues can result in prolonged shutdowns, revenue loss, and potential environmental or safety hazards.
Filter Membrane Predictive Maintenance
Ineffective Clean-In-Place (CIP) procedures or fouling of filter membranes can lead to various operational challenges, including increased cycle times, lost yield, or poor product quality. Additionally, detecting and modeling long-term deterioration in filter membrane performance poses significant challengess. This use case focuses on developing predictive maintenance strategies for filter membran...
Compressor Health Monitoring and Maintenance
Inability to detect and anticipate compressor performance issues can have detrimental effects, including unplanned shutdowns, revenue loss, and safety hazards.This use case aims to address these challenges by implementing comprehensive health monitoring and maintenance strategies for compressors. By leveraging advanced monitoring technologies, we can proactively identify potential issues, minimize...
Leaching Rate Prediction in Manganese Ore Processing
Developing an accurate predictive model for the complex nonlinearity of electric-field-enhanced pyrolusite leaching presents a significant challenge. The manganese leaching process involves crushing and grinding the pyrolusite ore (MnO2) to increase its surface area. This use case focuses on creating a predictive model for leaching rates in manganese ore processing. By addressing the nonlinear dyn...
Optimizing Flash Evaporator Performance with Predictive Maintenance
In the chemical industry, flash evaporators are crucial assets that require careful monitoring and maintenance to minimize runtime failures and downtime. However, the operations plant team faces numerous operational challenges due to frequent failures of critical assets like evaporators. This use case focuses on implementing predictive maintenance strategies for flash evaporators to address these ...
Bearing Failure Analysis
The recent bearing failures in the induced draft fan of the boiler system at a power plant have raised concerns. The challenge lies in determining the root cause, especially with limited events and no deviations observed in other critical variables. This use case focuses on conducting a comprehensive analysis of the bearing failures to identify the underlying cause. By leveraging advanced analytic...
Identification of Internal cracks in Aluminium Billets
Detecting internal cracks in aluminium billets poses significant challenges due to the complexity of crack formation and the difficulty in maintaining process parameters at optimal levels. Conventional methods such as failure analysis or ultrasonic testing are either time-consuming or costly, further complicating the inspection process.
Energy Analysis & Optimization of Kneader in Green Anode Plant
The kneading process in a green anode plant involves numerous parameters and complex dynamics, necessitating a comprehensive understanding of its intricacies. However, identifying the parameters that most significantly impact the energy consumption of the kneader and cooler remains a challenge. This use case focuses on conducting energy analysis and optimization of the kneader. By delving into the...
Pitch Demand & Anode Density Prediction in Green Anode Plant
Anode density is intricately linked to the composition and properties of anode paste, where pitch plays a pivotal role. Accurate prediction of pitch demand is crucial to ensure optimal availability for production while achieving desired anode density. This use case tackles the challenge by developing predictive models to forecast pitch demand and anticipate anode density. Download the PDF to explo...
Chemical & Structural Properties Analysis of Coke & Pitch
Understanding key parameters like quinoline insoluble, softening point, VBD, real density, and moisture content is vital for assessing coke and pitch quality. However, statistical metrics for these parameters can significantly impact production, potentially affecting anode and metal quality. This use case explores the analysis of these properties and their production impact.
Rolling Process Optimization for Electrical Steel
Achieving high magnetic flux density and low magnetic core loss in electrical steels is contingent upon various metallurgical and operational factors, including texture, grain size, chemical composition, and coiling temperature. However, optimizing the rolling process to meet these requirements presents a significant challenge. This project focuses on addressing this challenge by optimizing rollin...
Data Driven Root-Cause Analysis
The occurrence of two electrodes being remelted from a single ingot has resulted in the rejection of defective ingots, with one deemed acceptable and the other rejected due to transverse cracks. This project aims to conduct a thorough root-cause analysis using data-driven methodologies. By analyzing relevant data points and patterns, we seek to identify the underlying factors contributing to the d...
Nozzle Clogging Prediction during Continuous Casting
Nozzle clogging is a major contributor to oxide inclusions in steel, impacting the cleanliness of the final product. These clogs frequently interrupt the casting process, resulting in downtime and diminished productivity. This project is dedicated to developing predictive models for anticipating nozzle clogging during continuous casting. By leveraging historical data and advanced analytics, operat...
Silicon Content Prediction of Hot Metal in Blast Furnace
The silicon content of molten iron serves as a crucial indicator of temperature trends within a blast furnace. However, the wide variation in silicon content and the time delay in offline analysis pose challenges for operators in assessing the furnace's thermal operating conditions. This project focuses on developing predictive models to anticipate silicon content in hot metal, leveraging real-tim...
Hanging Prediction in Blast Furnace
Operating under intricate and dynamic conditions, blast furnaces are susceptible to hanging events influenced by numerous parameters. However, the interior of a blast furnace is inaccessible and challenging to monitor directly, hindering the real-time detection of hanging incidents. This project focuses on developing predictive models to anticipate hanging events in blast furnaces. By leveraging a...
Predictive Maintenance of Heat Exchanger
Fouling, the unwanted deposition of materials on heat transfer surfaces, poses a significant threat to the efficiency of heat exchangers in process heating and cooling applications. By insulating heat transfer surfaces and impeding heat exchange, fouling undermines overall efficiency. Predicting the end-of-cycle (EOC) for a heat exchanger due to fouling is a persistent challenge across industries....
Predict Conveyor Belt Tensioning Date
Predicting the optimal conveyor belt tensioning date is essential in the mining industry, where conveyor belts play a critical role in material transport. Over time, belts can stretch and wear, necessitating precise tension adjustments to prevent misalignments and failures. Accurate predictions help ensure smooth operations and minimize costly unplanned downtime.
Iron Ore Quality during Mining Operations
In the realm of mining operations, predicting the final quality of iron concentrate is pivotal for optimizing production processes. This project focuses on forecasting the percentage of silica present in the iron ore concentrate, a key determinant of its quality. By leveraging real-time data and predictive modeling techniques, engineers can anticipate silica levels in the concentrate, enabling pro...
Cement Blaine (Fineness) Prediction
Maintaining optimal fineness in cement production is paramount for ensuring product quality and meeting production targets. However, conventional lab testing methods often entail significant time delays, impacting the timely control of Blaine size and potentially leading to compromised quality, missed production targets, and revenue loss. This initiative focuses on leveraging predictive modeling t...
Pump Health Monitoring
In the pursuit of operational excellence, this analysis focuses on optimizing pump performance to mitigate prolonged shutdowns and revenue loss. By implementing scalable, maintainable, and repeatable monitoring approaches, it aims to enhance economic performance, safety, reliability, and compliance. Identifying failure events and their durations facilitates timely corrective actions, enabling orga...
%O2 Prediction in the Flue Gas in Furnace Operations
In the domain of fired heater operations, accurate monitoring and prediction of oxygen levels in flue gases play a critical role in ensuring operational efficiency and safety. This project focuses on conducting correlation and trend analysis to identify key process parameters (KPIs) that influence oxygen concentrations in flue gases. By examining factors such as fuel composition, combustion temper...
Debutanizer Column (Distillation Column) monitoring
Effective monitoring of a debutanizer column is essential for maximizing the LPG content in the top product and optimizing overall distillation performance. Continuous monitoring of the C4 fraction in the bottom products ensures process efficiency and quality control. Implementing a predictive model to estimate the butane fraction in the bottoms enhances performance by allowing for real-time adjus...
Heat Exchangers Performance Monitoring
Predicting the end-of-cycle (EOC) for a heat exchanger due to fouling is a persistent challenge for refineries. By proactively forecasting when a heat exchanger requires cleaning, refineries can implement risk-based maintenance planning, optimizing processing rates, and reducing operating and maintenance costs. Historically, engineers had to manually combine data entries in spreadsheets, spending ...
Reduce your Carbon Footprint using Smart Optimizer Framework (ANN)
The global target of 1.5 C is now becoming a major focus for every industry, enacting strict corrections to reduce the carbon footprint across the value chain. Especially when it comes to process-manufacturing space (oil and gas, metals/mining, chemicals, pharma), we have reached to an alarming situation, where control of the emissions and optimization of the resource consumption is a must to have...
Connected Analytics for Sustainability in Refinery Operations
With the advent of Technology, Oil and Gas Industry has transformed itself to adapt the best of analytics practices to extract maximum profit, without much expense at the productivity losses. Where, analytics practices include the use of Machine Learning/AI, Hybrid Modeling, Advanced Process Control (APC) and APM to mitigate the existing challenges of production losses, optimization, asset availab...
Digital Transformation – Revolutionizing the Process Industry
“Technology is best when it brings people together” “Technology is best when you use it wisely” “Data is the real evidence” That’s right, today in this world of the digital era, we are all surrounded by digital technologies, tools & data. Everyone of us is now becoming a part of it, some driving it, and some being driven by it. For some, it’s incremental and for some it’s disruptive. But we al...
Chemical 4.0 Solutions and Implementation Partner
Tridiagonal Solutions Chemical 4.0 practice leverages Industry 4.0 tech stacks to implement Manufacturing Excellence and Digital Transformation solutions. We bring a combination of tools, techniques, expertise, and solutions to support various initiatives (such as APM, APC, Digital Twin, Remote monitoring, Sustainability, etc.) of our customers. We work with a strong partner ecosystem and its stat...
80-20 Principle – A Key Metric to apply in Manufacturing Data Analytics
You can’t avoid it now! – The opportunity landscape of Data Analytics is increasing day-by-day in every industry. Many organizations have done huge investments in building and aggregating a data layer, whether it is in MES, Historian or a data lake. The value of data is being unlocked and leveraged for getting better insights. The innovative organizations are exploring the complexity of the proble...
Achieving Operational Excellence in Metals and Mining with Data Fabric Implementation
In the dynamic and complex landscape of metals and mining operations, achieving operational excellence is crucial for sustaining competitiveness and driving long-term success. The effective implementation of a data fabric architecture, supported by an ISA95-based equipment hierarchy, offers an opportunity to incrementally transform implementation of solution to streamline processes, optimize resou...