Articles

Dr. Dhatchana Moorthy - Director, EinNext Biosciences

In the ongoing quest for sustainable energy solutions, hydrogen emerges as a vital catalyst, embodying the potential to revolutionize our energy landscape. Hydrogen, the most abundant element in the universe, holds a promise that can redefine the way we power our world. Hydrogen can be harnessed through various pathways - from reforming natural gas to electrolysis powered by renewable energy. This adaptability allows for integration with diverse energy systems, making
hydrogen a vital enabler of a seamless and sustainable energy transition.

Hydrogen, as a clean and versatile energy carrier, has the potential to replace fossil fuels in various sectors, significantly reducing carbon emissions and playing a pivotal role in achieving a carbon-neutral global economy by 2050. The International Renewable Energy Agency predicts that the hydrogen economy will account for 12 percent of total energy consumption by 2050, and global warming remains below 1.5 degrees Celsius

Hydrogen can be produced through various processes, including thermochemical, electrolytic, direct solar water splitting, and biological processes. Thermochemical processes release hydrogen from organic materials like fossil fuels and biomass, while electrolytic processes use electricity to split water into hydrogen and oxygen. Though hydrogen can be produced from many sources, large-scale hydrogen value chains are yet to be established. This is because sustainable hydrogen production, storage, transportation, and distribution strategies are lacking.

Process modeling systems for hydrogen production involve various steps to optimize production efficiency and sustainability. These include selecting and preparing feedstocks, understanding reaction mechanisms and kinetics, conducting thermodynamic analyses, integrating and optimizing processes, designing heat exchangers, selecting materials for compatibility and durability, and optimizing hydrogen purification and separation methods.

On the other hand, systems engineering for hydrogen production is a comprehensive approach that involves designing, analyzing, and optimizing the entire process. It integrates various components, technologies, and disciplines to create efficient, reliable, cost-effective systems. Key aspects include system architecture, requirement analysis, modeling and simulation, control systems and automation, reliability and redundancy, lifecycle management and optimization,
interdisciplinary collaboration, and cost-benefit analysis.

EinNel Technologies develops a comprehensive architecture, analyzes requirements, uses advanced modeling and simulation tools, implements control systems, prioritizes reliability and redundancy, and continuously monitors and optimizes performance throughout the hydrogen system’s lifecycle. Interdisciplinary collaboration among our data science teams ensures a holistic approach.

With a legacy of innovation in data-driven computer-aided engineering (CAE), EinNel technologies is at the forefront of designing and modeling hydrogen-powered solutions, optimizing fuel cell and internal combustion engine systems to drive the future of automotive excellence. At EinNel, we strongly believe in harnessing hydrogen’s potential in energy production, transportation, aerospace, industry, and power generation, pivotal to revolutionizing and decarbonizing our world.

Niveda R - QA Manager, Software

AI-powered testing tools can analyze large datasets and identify patterns and anomalies that might be overlooked by human testers. They can also perform repetitive tests and simulations at a faster pace, significantly reducing testing time and costs. The EinNel team has implemented an AI-powered tool for visual testing and monitoring.

This AI-based technology can evaluate various user interface elements and offer visual webpage validation. Given that design assessment is typically a task for humans, these tests can be challenging to validate. Automated testing can handle several tasks that would be difficult for a single human, such as collecting screenshots and evaluating loading times.

Impact of AI in QA,

Through the use of this AI tool, EinNel has achieved several significant benefits, including increased test coverage and reduced maintenance efforts. We have accelerated both functional and visual tests by a factor of 30 compared to manual processes. Additionally, we have seamlessly integrated the AI tool into the existing EinNel automation framework by establishing a baseline. As a result, EinNel has effectively integrated AI technology into the software quality assurance field to harness the advantages of eliminating duplicate test cases, addressing high-risk and error-prone areas in applications, and predicting key test parameters for future tests.

Mercy Densingh - Data Science Engineer

A thermal power plant comprises a complex facility consisting of interconnected subunits, including a boiler, turbine condenser, cooling system, and electrical generator, all collaborating to generate electricity seamlessly. However, these power plants often encounter numerous challenges such as clinker issues, condenser fouling, and problems with ash handling, arising from various factors. These challenges significantly impact the overall performance of the thermal power plant, leading to increased operational costs.

The operational and informational data generated by these subunits amount to a substantial volume—approximately 5 lakh records per day—collected in real-time by data acquisition systems. This data can be very helpful in resolving the challenges. If we carefully engineer and analyze this data, we can identify the relationship between various control parameters and the root cause underlying operational issues. Yet, managing extensive datasets from power plant operations is a complex endeavor that demands profound expertise in cutting-edge AI and ML techniques.

The EinNel AI team is highly equipped to handle these sizable datasets arising from power plant operations. Our expertise extends to various data preprocessing and refined data modeling techniques. This proficiency not only uncovers the core issues but also adeptly predicts potential challenges, thus optimizing the overall performance of the power plant system. The benefits of this data-driven approach span beyond just enhanced operational efficiency and cover predictable performance, minimized downtime and expenses, optimal resource allocation, and well-informed decision-making facilitated by diverse data analytics. this data, we can identify the relationship between various control parameters and the root cause underlying operational issues. Yet, managing extensive datasets from power plant operations is a complex endeavor that demands profound expertise in cutting-edge AI and ML techniques.

Merlin Shakila & Lokesh B - Software & Data Science Team

Productivity stands as the cornerstone of every successful business endeavour, demanding the seamless integration of workflow and data orchestration. These twin pillars not only expedite processes but also elevate collaboration, communication, optimization, and adherence to regulatory standards. EinNel stands poised to revolutionize conventional methodologies by ushering in a pioneering platform tailored for workflow and data orchestration within the realm of industrial product design and processes. This article shines a spotlight on the prowess of EinNel’s software team in the domain of workflow orchestration and its attendant advancements.

At the heart of this platform lies a pivotal concept: Workflows. These workflows consist of a series of tasks that can be activated by a range of events. These events might encompass the identification of specific files within the Data Lake or could be scheduled occurrences based on predefined timeframes. Moreover, an API can be harnessed to initiate workflows from applications designed for iPad devices.

The EinNel team deeply ingrained in cloud architecture possesses an intimate understanding of the Extract, Transform, Load (ETL) process and Dashboard integration. The ETL process encompasses the extraction of data from diverse sources, its subsequent transformation into a structured format, and its final loading into a central repository – the Data Lakes. The platform functions as a data orchestrator, meticulously crafted to ensure the smooth and uniform circulation
of data across various departments.

However, the journey of data doesn’t culminate with the ETL phase. The EinNel team is implementing the data model development process, overseeing version control, automated deployments, and seamless integration with diverse Business Intelligence (BI) platforms. All these facets combine to craft a self-sustaining and scalable ecosystem.

EinNel, thus, extends its Data Orchestration and Workflow Orchestration solution across a spectrum of sectors, encompassing domains like Automobile Industries, Thermal Power Plants, and Pharmaceutical Industries to attain digital transformation.