Case Studies

The case study illustrates the successful implementation of digital transformation initiatives aimed at enhancing Overall Equipment Efficiency (OEE) and Overall Performance Efficiency (OPE). The project's primary objective was to digitize key performance indicators (KPIs) and establish an automated feedback loop to optimize organizational processes. Through a diagnostic approach, areas for improvement in OPE and OEE were identified and addressed systematically. An automated solution was deployed to monitor critical KPIs in real-time, providing operators with actionable insights for prompt decision-making.  Overall, the project exemplifies how digital transformation can empower organizations to achieve operational excellence through data-driven insights and continuous improvement initiatives.

In response to the need for enhanced quality control in multistage manufacturing, we successfully implemented a comprehensive solution using machine learning (ML) and expert system technologies. Operating parameters like temperature, pressure, concentrations, and mixing time were collected and analyzed to develop a robust ML model predicting defects. Integrated into a real-time monitoring system, the ML model provided quick feedback to the production team. Additionally, an expert system layer was implemented to offer detailed analyses of defect causes. This integrated approach reduced defective products, lowered operational costs, and enhanced customer satisfaction. The synergy between ML-driven defect prediction and expert system analysis showcased the adaptability and effectiveness of this approach, indicating its potential for elevating quality control practices in diverse manufacturing environments.

To meet the demand for enhanced process monitoring in fed-batch bioreactors, our project successfully integrated a first-principles bioreactor model with machine learning (ML) techniques. Initially, a bioreactor model was constructed using first principles, simulating various operating conditions to generate diverse datasets encompassing both successful and unsuccessful batches. This data served as the foundation for training our ML classification model. The ML model, developed to discern between good and bad batches, demonstrated its efficacy during the entire 48-hour batch time. Upon deployment, the ML model effectively identified the batch quality at any given time instance, enhancing real-time monitoring capabilities. This integrated approach, combining first-principles modeling with ML, showcased the project's success in achieving accurate and timely detection of batch quality variations in a fed-batch bioreactor setting.

The project's primary objective is to develop a training simulator utilizing augmented reality (AR) and virtual reality (VR) technologies. This simulator replicates an industrial environment, enabling trainees to interact with various industrial processes and control systems. Through a VR headset, trainees can engage with simulated machines and equipment commonly found in industrial settings, allowing them to operate and control the system. Using an Human-Machine Interface (HMI), participants can fine-tune Proportional-Integral-Derivative (PID) controllers under different scenarios and conditions. Overall, the simulator serves as an immersive educational tool for teaching trainees how to effectively manage and optimize industrial processes within a realistic virtual environment.

The primary aim of this project is to develop a training Hardware-in-the-Loop (HIL) simulator for a remote experimental platform, facilitating hands-on exploration of AC motor control and performance analysis. This platform functions as a seamless HIL interface, integrating a Programmable Logic Controller (PLC) and a Human-Machine Interface (HMI). Through this setup, trainers gain the capability to manipulate and assess the behavior of the AC motor both locally and remotely. By promoting accessibility in both settings, this initiative strives to enrich learning experiences and cultivate deeper understanding within the realm of AC motor systems.

​This project represents an innovative IoT-based solution designed to empower plant operators by enabling remote operation and monitoring of water transfer pumps situated in distant locations. At its core, the system features a user-friendly web interface accessible via mobile devices, as well as a robust desktop server stationed at the control/base station. Complementing these components is a remote station panel deployed at the remote site, facilitating seamless communication between the control/base station and the well stations. Critical data gathered from the well stations is efficiently transmitted to the control/base station through the intermediary of the remote station, ensuring a dependable flow of information.