TL;DR

• Sustainability initiatives require efficient use of limited resources.
• Automation enables measurable improvements in energy use and emissions.
• Monitoring WAGES systems is key to identifying waste.
• Modern control and reliability tools reduce inefficiencies and failures.
• Advanced control and AI unlock further sustainability gains over time.

Why this matters now

When it comes to a need for more sustainable operations, the life sciences industry is no exception to current trends. But just because building more sustainable operations is important doesn’t mean it is easy.

As Emerson’s Esther Henoch and Philip Vodopiutz explore in a recent article in European Biopharmaceutical Review:

“Effective sustainability initiatives require resources, such as time, money, knowledge and personnel, so any investment in these must be efficient and impactful.”

This highlights a central tension for many teams—balancing sustainability goals with limited resources.

Takeaway: Sustainability efforts must deliver measurable impact without overextending limited resources.

The answer to navigating this challenge is automation. Automation technology can deliver significant sustainability improvements for both new projects and existing infrastructure.

Monitoring control and reliability

One of the most common places where facilities can make progress is in monitoring water, air, gases, electricity, and steam (WAGES). Achieving this effectively requires automation technologies spanning both process control and asset reliability.

In the process, tools like Emerson’s digital valve controllers (DVC) monitor valve performance to ensure wear or poor configuration are not creating inefficiencies that lead to waste.

“They help ensure efficient operation, support energy savings and provide early warnings of potential fugitive emissions risks.”

This provides early insight into conditions that would otherwise increase energy use or emissions.

Takeaway: Intelligent valve monitoring reduces waste and helps prevent emissions early.

In addition, having a modern control system is key to monitoring process performance and health:

“Modern control systems can check the health of control loops and report those not operating to specification or relying on inefficient manual intervention. Emerson’s analysis during energy surveys indicates that up to 70% of control loops may not be performing correctly due to field issues, poor tuning or poor design.”

This underscores how widespread inefficiencies can be in legacy systems.

Takeaway: Poorly performing control loops represent a major hidden source of inefficiency.

Modernizing to a DeltaV™ Distributed Control System not only improves control and visibility, but also creates an opportunity to reassess operational strategy and process design.

“Following an audit, service engineers can recommend solutions that may be as simple as adjusting software parameters, retrimming a valve to match current process conditions or adding a DVC.”

Many improvements are incremental, but still highly impactful.

Takeaway: Small optimization changes can deliver meaningful sustainability improvements.

But improving control alone is not enough. Teams must pair modern control with modern reliability technologies. Tools like wireless vibration monitors, wireless acoustic transmitters, and non-intrusive ultrasonic sensors help monitor asset health and detect issues before failure.

This enables maintenance to be scheduled proactively, reducing the risk of energy losses or emissions caused by degrading equipment.

Takeaway: Predictive maintenance reduces waste by addressing inefficiencies before failure occurs.

Future-forward

As teams establish strong foundations in control and reliability, they can begin exploring more advanced technologies to capture additional efficiency gains.

With fully instrumented systems, teams can leverage intelligent machinery health software to perform higher-level analytics and enable predictive maintenance at scale.

Many organizations are also adopting advanced control systems that dynamically adjust processes to maintain optimal performance:

“Advanced control software builds a model based on historical process data and dynamically adjusts loop parameters to optimise overall performance. These models can adaptively and continually learn and optimise their weights leading to increasingly precise models. Artificial intelligence (AI) tools layered on top of advanced control further enhance decision-making by revealing ‘hidden’ closed-loop relationships. Advanced control is well suited to applications such as distillation (including solvent recovery) or complex steam systems, both common in life sciences.”

This represents a shift from static optimization to continuous, adaptive optimization.

Takeaway: Advanced control and AI enable continuous optimization of complex processes.

Armed with advanced control and AI-driven analytics, teams can build self-diagnosing systems capable of responding more effectively to changing conditions across both the facility and the broader enterprise.

Start small, scale fast

Ultimately, teams need to define a starting point and begin implementing automation to meet immediate needs. Not every sustainability project must be a “net-zero emissions by 2030” roadmap. Even small changes in operation can have significant impacts on energy use, emissions, and waste, and those small changes can deliver significant return on investment and help teams drive toward the operational excellence that will secure competitive advantage in the years ahead.

Takeaway: Incremental improvements can deliver immediate ROI while laying the foundation for long-term sustainability.