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Engineering the Change: Creating Impact with Sustainable Solutions

Engineering the Change: Creating Impact with Sustainable Solutions

Q&A with Experts on Real-World Engineering Challenges and Opportunities in Sustainability

In an era where every organization—from startups to Fortune 500 companies—is pledging climate commitments, the reality of meeting net-zero goals still feels elusive. Only 18% of companies are on track to hit their 2050 targets, according to a recent Accenture report. So, what’s missing?

In this insightful discussion, Mr. Majunath Rao, Director, Utthunga, and Dr. Shankar, Co-founder & Director, GyanData Pvt. Ltd. sat down to unpack how engineering can create scalable, sustainable impact across industries. Here’s a seamless Q&A-style recap that dives deep into challenges, practical solutions, and industry use cases.

Understanding the Area of Energy Management, Assessments and Audits

Mr. Manjunath Rao, Director, Utthunga

The common perception that “energy” refers only to electrical energy is inaccurate. In reality, electrical energy accounts for just 8% of the total energy usage. It’s important to distinguish between the different contributors that make up electrical energy.

Beyond electricity, there are various forms of energy such as thermal energy, hydraulic (water) energy, and internal energy. Each of these plays a significant role in the broader energy system, especially in industries like oil and gas, petrochemicals, and chemicals.

The oil and gas sector is highly mature in terms of energy management. These industries not only generate energy but are also accountable for how it is consumed. They are experienced in collecting data and optimizing energy use efficiently.

In contrast, the petrochemical sector represents a medium level of investment in energy conservation and management practices. The chemical industry, however, faces greater challenges. It is highly fragmented, consisting of many small sectors, which makes implementing uniform energy practices more difficult.

To address this, Utthunga is developing a simplified and accessible energy management approach tailored for the chemical sector, helping them manage energy more effectively with minimal complexity.

It’s important to note that we do not physically shift energy from one place to another. Utthunga’s primary goal is to decarbonize energy systems, which means replacing fossil fuel-based energy with renewable and alternative energy sources. This is essential because, regardless of the form energy takes, the carbon footprint remains unless the source changes.

Energy management should be approached systematically. While many claim to manage energy, in practice, their efforts often stop at basic measures like installing LED bulbs. Our approach goes much deeper — we focus on comprehensive energy conservation across all energy types.

Utthunga’s core strategy begins with an energy audit, which we divide into four distinct phases to ensure thorough and actionable insights.

Our Four Phases of Energy Audit

We break energy down into four phases to provide an actionable roadmap. These include:

1. Data Collection: Gathering relevant data about energy use in the plant or facility.

2. Baselining and Benchmarking: Analyzing equipment efficiency, often comparing older assets like 30–40-year-old compressors with current industry standards.

3. Finding Opportunities: Using simulations and scientific methods, we identify where energy can be saved—whether by tweaking processes or optimizing utilities.

4. Implementation Roadmap and ROI Analysis: Building a clear plan showing investments needed, expected savings, and return on investment (ROI), which is crucial for decision-making.

Energy Efficiency Optimization Process

An Example of How a Four-Phase Energy Audit Works in a Petrochemical Plant

Mr. Manjunath

During a visit to a petrochemical plant, Utthunga discovered that the plant was incurring a monthly energy loss of ₹1.78 crore. This revelation served as a major eye-opener for the management.

They promptly reached out to us for support, and we carried out a comprehensive energy audit. One of our key findings was that their largest thermic heater was operating at just 18% of its actual capacity. While the company believed the heater’s efficiency to be 88%, our assessment revealed it was only 55%.

In addition, we noticed that chillers and water monitoring systems were poorly managed, contributing to further inefficiencies.

Over a two-week audit period, Utthunga developed and delivered a solution with a return on investment (ROI) within 12 months. Our intervention included process optimization, such as reducing batch reaction times by 10% to 15%, resulting in significant performance gains.

Utthunga also benchmarked their equipment against industry standards. Some of their machines were decades old, creating a substantial performance gap when compared to modern industry best practices.

The Role of Engineering in a Plant’s Lifecycle

Mr. Manjunath

Everything Begins with Plant Design

The foundation of sustainability in any industrial setup lies in its plant design. This is where the seeds of long-term efficiency and sustainability are sown. It’s not about building a large facility and then operating it at a reduced scale — rather, designing a plant that is fit-for-purpose is what truly matters.

Design Stage: The Ideal Moment for Sustainability Planning

Maximum sustainability value can be achieved during the design phase. Unfortunately, many companies wait until after construction to conduct an energy or sustainability audit — by then, significant investments have already been made, and the opportunity to embed sustainable practices from the start is lost.

This critical planning falls under basic engineering, which then transitions into detailed engineering and construction. This stage demands maximum attention, as any oversight here can lead to long-term inefficiencies and missed sustainability goals across the plant’s lifecycle.

Operations: The Heart of Sustainability

Once the plant is operational, the operations phase plays a massive role — contributing nearly 60% to 70% of a facility’s overall sustainability. This includes factors such as:

  • Energy use
  • Waste recovery
  • Environmental impact

Operational inefficiencies such as poorly configured control valves, ineffective process logic, unnecessary shutdowns, and frequent startups all add up, negatively affecting both performance and sustainability.

Maintenance: A Key Driver for Sustainable Plant Life

Maintenance practices are equally vital. Today, with the rise of predictive and prescriptive maintenance, we can anticipate equipment failures and arrange necessary logistics in

advance, significantly reducing unplanned downtime and increasing operational efficiency.

The Digital Edge in Sustainable Engineering

With the advent of digitization, the scope and effectiveness of sustainable engineering have grown exponentially. Digital tools and data analytics now enable better decision-making, smarter maintenance strategies, and optimized resource use throughout a plant’s lifecycle.

Sustainable Engineering with Digitalization

Understanding Optimization Areas in Plant Life Cycle and its Impact

Dr. Shankar, Co-founder & Director, GyanData Pvt. Ltd.

Process Design Changes Offer the Highest Gains

One of the most impactful ways to improve energy efficiency is through changes in process design. This may involve rerouting pipelines, adding a few heat exchangers, or making other system-level adjustments. While these modifications do require some investment, the payback period is typically between 6 months and 1 year. The energy savings, especially in thermal energy consumption, can be significant — ranging from 10% to 30%.

Shifting Focus: Thermal and Electrical Energy Utilization

Over the past 5 to 10 years, the industry’s focus has expanded beyond just thermal energy to include a more integrated view of both thermal and electrical energy usage. The challenge now is: how can we optimize combined energy consumption within a process?

In chemical processes, approximately 80% of energy consumption is thermal, while the remaining 20% is electrical. This presents an opportunity: if a portion of thermal energy usage can be shifted to electrical energy — in a cost-effective way — we can replicate the energy transition seen in the automotive industry, where internal combustion engines are being replaced by battery-powered electric vehicles.

Partial Shift Toward Electrification

While it’s not feasible to fully shift a chemical process from thermal to electrical energy, a partial transition is possible. If the electrical energy is sourced from renewables, this shift becomes not only technically viable but also sustainable and economical.

Evolving Tools: Modified Pinch Technology

To support this integrated approach, Pinch Technology — traditionally used for optimizing thermal energy — is now being adapted to also consider electrical energy. This evolution allows for more comprehensive energy integration strategies, enabling industries to maximize efficiency across both thermal and electrical domains.

Example:

A power plant boiler where hot flue gas exits containing leftover heat. Instead of wasting it, we transfer this heat to two places: the air used for combustion and the water fed into the steam tubes. You have two choices:

  • Heat the air first, then the water, or
  • Heat the water first, then the air.

It’s due to thermodynamics—heat transfer depends on temperature differences between streams, not just flow. So, if you heat the water first when the flue gas is hottest, you get more heat recovery. This subtle change in configuration can significantly reduce thermal energy consumption.

This approach is broadly applicable to any plant where heat recovery matters. By reviewing existing heat exchanger setups, plants can often identify simple configuration changes that yield significant energy savings. Tools like pinch technology help formalize this analysis, identifying where savings are possible, estimating costs, and calculating payback times.

Energy Optimization in Complex Processes like Distillation

Distillation columns separate components with small boiling point differences (e.g., 10–30°C). They require lots of thermal energy supplied at the bottom (reboiler) and cooling at the top. Because of the narrow temperature difference, these columns use a lot of energy.

Example:

In vapor recompression technology, the vapor leaving the top is compressed to raise its temperature, then used to supply heat at the bottom. This heat integration reduces the external heat needed.

Does Compressing Vapor Mean Using More Electricity

Yes, but this is a trade-off—sacrificing some electrical energy to save a larger amount of thermal energy. Given the increasing availability of renewable electricity, this approach improves both cost-effectiveness and sustainability.

Common Use of Vapor Recompression Technology

Vapor recompression technology is used in over 20 distillation processes involving close boiling mixtures, such as:

  • Splitting C2 (ethylene/ethane) and C3 (propylene/propane) streams in refining and petrochemicals
  • Methanol-water separation
  • Benzene-toluene separation

Evaluating the Economic Viability of Using Such Technology

Using pinch analysis and simulation tools, engineers estimate energy savings, electrical power needs, investment costs (like compressors), and calculate payback periods, often around 6 months, ensuring decisions are financially sound.

Which Chemical processes are Prime Candidates for Electrification now?

These include processes involving low temperatures and high pressures, such as:

  • Energy liquefaction
  • Air separation
  • Liquid air energy storage

These processes can realize significant cost and carbon savings today by integrating electrical technologies.

What Should the Chemical Industry do Right Now?

The industry shouldn’t wait for 100% renewable electricity. It can start by:

  • Optimizing thermal systems through pinch analysis
  • Selectively shifting from thermal to electrical energy use
  • Implementing technologies like vapor recompression

These steps reduce costs, cut emissions, and position companies well for a renewable-powered future.

This webinar highlighted how smart process design and technologies like pinch analysis and vapor recompression can significantly cut energy use and costs in the chemical industry. Even simple changes can yield big savings, while electrification offers a path toward greater sustainability today.

For a deeper dive, watch the full webinar here

Feel free to share your questions or connect with us on LinkedIn!

AI-Driven Threat Detection: The Future of OT Cybersecurity Solutions

AI-Driven Threat Detection: The Future of OT Cybersecurity Solutions

In 2024, a major U.S. manufacturer of printed circuit boards fell victim to a ransomware attack that escalated from a simple phishing email to full network compromise in less than 14 hours. The financial impact was devastating — losses estimated at $17 million. What made this attack particularly damaging was its focus on Operational Technology (OT) systems — the machinery and control processes that keep factories and critical infrastructure running. Unfortunately, this incident is far from isolated; it highlights a growing and alarming trend.

Cyberattacks targeting OT environments have surged sharply. Recent data shows that 73% of organizations reported intrusions affecting OT systems in 2024, up from 49% just a year before. What’s more concerning is the rise of AI-enhanced attacks—threats that leverage automation and machine learning to carry out operations faster and on a larger scale. These AI-powered attacks now cut the time needed to deploy sophisticated ransomware from hours down to mere minutes.

Traditional cybersecurity strategies are struggling to keep up, especially given the unique challenges OT environments face—outdated equipment, limited patching options, and the need to avoid operational downtime at all costs. Against this backdrop, AI-driven threat detection has become a crucial pillar of modern OT security.

AI’s Role in Enhancing OT Security

Securing OT environments demands more than conventional IT security tools. Unlike typical IT systems, OT relies on specialized hardware and protocols that were often never designed with cybersecurity in mind. This is where AI makes a meaningful difference by bridging critical gaps.
i. Advanced Threat Detection and Anomaly Identification: AI systems analyze vast streams of data coming from OT devices network traffic, system logs, and sensor readings—to spot abnormal patterns that could indicate a breach. Machine learning algorithms build an understanding of what “normal” looks like and then flag deviations, enabling early and accurate detection of even subtle threats.
ii. Predictive Maintenance to Prevent Downtime: Beyond security, AI improves operational reliability. By analyzing equipment data, AI can predict when a machine might fail, allowing organizations to fix problems before they happen. This not only keeps systems running but also reduces risks caused by unexpected breakdowns.
iii. Automated Incident Response: When an attack does occur, AI can step in to accelerate response efforts—identifying the scope of the breach, isolating compromised components, and kicking off remediation processes. This automation shortens response times and helps prevent damage from spreading.
iv. Enhanced Vulnerability Management: AI tools continuously scan OT networks and systems for vulnerabilities, helping security teams prioritize the most critical risks. This focused approach makes security efforts more effective and manageable.
v. Explainable AI for Transparent Decision-Making: One concern with AI is that it can sometimes act like a “black box,” making decisions without clear reasoning. Explainable AI (XAI) addresses this by providing insight into how decisions are made, which is essential for building trust and ensuring compliance in OT environments.
vi. Real-Time Operational Insights and Risk Assessment: AI doesn’t just spot threats—it continuously evaluates risks based on real-time data, helping teams prioritize protections around the most critical assets. This dynamic risk assessment balances security needs with operational continuity, a must for industries like energy and manufacturing.
vii. Seamless Integration with Industrial Control Systems: Modern AI solutions are designed to work alongside legacy systems such as SCADA and PLCs without causing disruption. This compatibility is critical, especially for sectors relying on older equipment that cannot be easily replaced but still needs robust protection.

Efficiency Gains Through AI

The benefits of AI extend beyond enhanced security. Organizations are also seeing significant efficiency improvements:
  • Reduced Alert Fatigue: AI filters out false alarms and focuses attention on genuine threats. For example, Siemens Energy reported a 40% drop in false alerts after deploying AI-based detection.
  • Faster Threat Detection: In mature environments, AI has cut average breach detection times from over 200 days to under 40, giving teams a crucial time advantage.
  • Augmented Human Expertise: Automating routine investigations and triage lets security staff focus on strategic tasks. Some manufacturing clients have seen a 25% reduction in incident management time after introducing AI tools.

What Leading Enterprises Are Doing

Across industries like manufacturing, energy, utilities, and logistics, organizations are quietly but steadily adopting AI-driven OT security solutions. Drawing from both our client work and wider industry observations, here’s how AI is being used effectively to secure OT environments in critical sectors:

  • At a major European logistics hub, an AI system correlates data from OT equipment—such as crane controllers and fuel systems—with IT security signals. This enables the security team to significantly reduce investigation times and proactively block credential misuse attempts before they escalate into operational disruptions.
  • A large utility provider in the Middle East uses passive network monitoring powered by AI to safeguard legacy SCADA systems that cannot be patched. We’ve supported a similar client in deploying this approach, achieving near real-time threat detection across hundreds of substations while keeping systems online.
  • In North America, one manufacturer’s AI-driven analytics flagged an unusual pattern in robotic arm movements—not as a mechanical error, but a possible cyber manipulation. Several of our manufacturing clients have since adopted similar AI capabilities to deepen their visibility and response.
  • Organizations operating under European NIS2 and GCC’s NCA and NDMO frameworks are increasingly turning to AI not only to enhance security but also to meet regulatory expectations and lower cyber insurance costs.
Industry-wide, over 76% of Fortune 500 manufacturers and critical infrastructure providers have either implemented or are piloting AI-based OT threat detection. The most progress is seen in hybrid IT/OT environments, where AI helps unify fragmented teams and tools—a trend we’ve observed firsthand with multiple clients.

The Path Forward

OT systems are under pressure like never before. With threats becoming faster, smarter, and harder to detect, relying solely on conventional tools is no longer enough. AI-driven threat detection is proving to be a critical layer in modern OT security—one that helps organizations detect subtle anomalies, respond quickly, and reduce downtime without disrupting operations.

But putting AI to work in OT isn’t just about adopting new technology. It’s about knowing where it fits, how it behaves around legacy systems, and what risks actually matter on the plant floor or control room.

That’s where Utthunga’s cybersecurity solutions make a real difference. Working with leading industrial clients, we deliver AI-powered threat detection capabilities built specifically for complex OT environments. From passive monitoring of legacy systems to intelligent threat correlation across IT and OT, our cybersecurity solutions are helping organizations stay a step ahead of threats while keeping operations secure and resilient.

Utthunga and Data Gumbo Launch UTT-DataGumbo for Industrial AI & Automation

Utthunga LLC and Data Gumbo Intelligent Systems today announced the launch of UTT-DataGumbo, a strategic joint venture uniting Utthunga’s 1,200-strong industrial engineering team and AI analytics with Data Gumbo’s automated smart-contract workflows and sustainability frameworks.

Established under a non-binding framework, UTT-DataGumbo will accelerate transformation across energy, manufacturing, chemicals, and metals & mining sectors. The platform’s modular architecture and standardized connectors simplify deployment across industrial verticals—enabling consistent workflows, automatic policy validations, and reduced integration overhead as clients scale operations from pilots to enterprise-wide.

Read full article here

How agentic AI is transforming industrial cybersecurity

With the evolution of the cyber world, cybersecurity threats have evolved in lockstep, mutating from simple malware attacks to highly sophisticated ransomware, including state-sponsored threats, each threatening to derail industrial operations with ramifications of a never-before kind. The advancement of these threats has also spawned the emergence of equally advanced security models, which incorporate AI, ML, and real-time monitoring to negate the potential impact of these threats and keep operations on track.

Agentic AI – an autonomous system capable of independent decision-making while working within specific environments – has emerged as yet another modern-day AI model that can transform industrial cybersecurity in revolutionary ways.

Read full article here

Decarbonising the Oil and Gas Industry

Decarbonising the Oil and Gas Industry

The oil and gas industry lies at the centre of global energy production, but its environmental footprint is impossible to ignore. It’s estimated that greenhouse gas emissions from this sector account for about 15% of the planet’s energy-related emissions. With growing international pressure to decarbonise, the time for the industry to act decisively has never been greater.

What makes this challenge particularly daunting is the breadth of the industry’s emissions profile. Virtually every stage of the value chain contributes to the problem, from the combustion in boilers, heaters, and flares to indirect emissions produced by compressors and pumps. These operational necessities result in a hefty carbon footprint. On top of that, fugitive emissions—unintentional leaks from pipelines, scrubbers, and valves—make managing emissions not only a complex but urgent task.

Read full article here

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