By Ryan Rennie, from the Spada-Rennie Group
In this article I explore the impact of artificial intelligence (AI) on Building Management Systems (BMS) in pharmaceutical plants, with a special focus on fully integrated smart building systems.
Ryan Rennie, from the Spada-Rennie Group. Supplied by Ryan Rennie
I ‘ll discuss the comprehensive benefits these systems offer, from optimised energy usage, integration of renewable energy, water conservation, to improved air quality and climate adaptability. Additionally, I’ll explore the potential risks, such as job displacement, privacy concerns, and cybersecurity threats, and discuss effective mitigation strategies. I will uncover how AI and smart technologies are reshaping pharmaceutical BMS, enhancing operational efficiency, sustainability, and market access across the continent.
1..Benefits of AI in facility design & BMS
a. Optimised system design Generative design
The design of a facility has only one constant: change. It is therefore important to have a signed-off first revision Good Manufacturing Practice (GMP) architectural layout and confirmation of the User Requirements Specifications (URS) early in the design phase. All the design work related to services like HVAC, electrical and utilities then have a baseline. Before the first services design is complete a first change will inevitably come into play.
For instance, the client will change a process equipment supplier, and they need more space; the client will want to introduce another manufacturing line or process and omit another, etc.
Then all that work – and specifically from an HVAC perspective – will need to be redone. Remember that the whole HVAC design is based on a pressure cascade with carefully considered air volumes. Change one room and the design has to change. The impact of these changes on the overall project timeline is massive. The redesign is a highly time-consuming process that can be sped up dramatically with an integrated AI design system.
Current tools such as Autodesk’s Revit allow engineers to create optimised building designs that AI will be able to evaluate to find the most efficient options. AI can further assist in creating multiple design options, optimising for energy consumption, airflow, temperature control, and system cost. It can help engineers choose designs that balance performance with cost-efficiency.
Predictive modelling
AI enables more accurate predictive modelling by empowering architects and engineers to optimise cGMP manufacturing facility designs during the design phase by analysing large datasets such as historical weather data. It helps design HVAC systems that are more energy-efficient, cost-effective, and tailored to specific environments, factoring in aspects like occupancy patterns, local climate, and building usage.
AI identifies potential bottlenecks, minimises risks, and maximises operational efficiency, ultimately reducing construction timelines and costs.
Additionally, AI-driven design assists in resource allocation, layout optimisation, and energy efficiency, aligning facilities with sustainable and cost-effective practices.
Other AI tools that architects and engineers might utilise include text to image for idea generation, photo to sketch, image to video, augmented reality and process simulation.
b. Energy management
It is disturbing to see how often we go back to these sites after handover and find that the client has turned the BMS computer off and shut the door. The amount of information available on these systems; the need for one of two full time BMS operators to maintain the system; the requirement to continually adjust the setup of alarms and the need to constantly re-qualify the systems means that the client often underestimates what they are getting when installing a BMS system.
I recall numerous times that the technical managers of these sites would look at me with a look of resignation and show me their continuous stream of alarms that they simply cannot stay on top of with their available resourcing.
- Smart controls: AI enables HVAC systems to adapt to real-time conditions by using sensors and data analytics to control temperature, humidity, and airflow. It ensures that systems only operate when necessary, reducing energy wastage.
- Dynamic scheduling: AI systems can predict heating and cooling needs based on weather forecasts, occupancy data, and building usage patterns. This helps to reduce energy consumption during low-demand periods.
c. Predictive maintenance
- Failure prediction: AI can predict and prevent potential failures by analysing data from HVAC sensors. This leads to preventive maintenance before breakdowns occur, reducing production downtime and extending the equipment’s lifespan.
- Optimised maintenance schedules: AI-driven maintenance schedules can be created based on system performance data, ensuring that components are serviced only when needed rather than following a fixed schedule.
- Condition monitoring: Continuous monitoring of critical systems such as HVAC, refrigeration, and cleanroom environments ensures they operate within required parameters, which is crucial for maintaining drug quality and compliance.
d. Compliance and environmental control
- Regulatory compliance: AI can assist in maintaining stringent environmental standards required in pharma manufacturing, such as constant temperature, humidity and pressure levels.
- Automated documentation: AI can streamline the documentation process required for regulatory compliance by automatically recording and organising data and assisting in audit preparedness.
- Anomaly detection: AI can detect deviations from set environmental conditions quickly, triggering alerts and corrective actions to prevent compromised product quality.
e. Cost reduction
- Reduced energy costs: AI can significantly reduce energy bills by optimising the operation of HVAC systems, adjusting settings automatically to minimise energy consumption during peak periods or when spaces are unoccupied.
- Lower maintenance costs: With predictive maintenance and real-time monitoring, costly emergency repairs can be avoided, and the overall cost of maintenance is reduced.
f. Security and access control
- Enhanced security: AI can improve security through advanced access control systems, facial recognition, and real-time monitoring of security footage.
- Incident response: AI can quickly detect and respond to security breaches, minimising potential disruptions or threats
In the pharmaceutical industry, biased decisions can lead to product quality issues or unjustified deviations from standard procedures. Image by montypeter on Freepik
2. Integrated smart building systems and environmental impact
We won’t be looking into the details of the Future of Smart Cities and Neighbourhoods but it is the next level of consideration. A brief review of this obvious future does however provide context to this article and the possibilities that exist.
Imagine the day when through smart interconnectivity your building not only manages its internal operations but also communicates with neighbouring buildings to optimise energy use for the entire area. This communication allows buildings to predict and adjust their energy needs collectively, enhancing efficiency on a much larger scale than single-building optimisation. Look into Toronto’s Quayside project or Amsterdam’s Smart City initiative for more information.
Then expand this concept to a city-wide scale. The concept of buildings communicating with each other to create smart neighbourhoods and cities is a fascinating and integral aspect of the future of urban development. AI and IoT integration in smart cities can lead to more sustainable urban living by intelligently managing resources and infrastructure. Reduced energy costs, improved air quality, and enhanced public services through better data and resource management.
A fully integrated smart building system brings together various technologies and systems (such as HVAC, lighting, security, and energy management) into one cohesive network that can communicate, adapt, and optimise performance. This integration has numerous benefits for the environment, including:
- Energy efficiency
- Reduced carbon emissions
- Water conservation
- Improved indoor air quality
- Material and resource efficiency
- Sustainable building operations
- Reduced waste and operational costs
- Climate adaptability
- Enhanced comfort with minimal environmental impact Now back to our localised smart pharmaceutical building.
a. Comprehensive integration
- Networked systems: Integrating HVAC, lighting, and security into one cohesive networked system allows for seamless communication and smarter overall building management.
b. Sustainability Practices
- Renewable Energy Integration: AI has successfully integrated renewable energy sources, such as solar or wind, to reduce reliance on fossil fuels. AI and IoT contribute to energy efficiency, reduced carbon emissions, and water conservation.
c. Data analytics and insights
- Advanced analytics: AI can handle vast amounts of data generated by BMS, providing insights that help in decision-making and strategic planning.
- Trend analysis: By analysing trends over time, AI can help predict future conditions and prepare the plant for various scenarios.
d. Operational efficiency
- Process optimisation: AI can analyse processes to identify inefficiencies and recommend improvements, ensuring optimal operation of plant systems.
- Resource management: AI helps in managing resources effectively by forecasting needs and automating inventory management.
3. Risks and mitigation strategies
a. Risks overview
There are 3 major risk categories: Human, Operational, Regulatory.
-Risks to humanity
- Automation and job displacement: AI-driven automation can lead to the displacement of workers who perform routine maintenance, monitoring, and management tasks, necessitating retraining and potentially leading to unemployment. Not what we want to hear in Africa.
- Skill gap: The shift towards AI-driven systems requires new skills, and there may be a gap between the skills of the current workforce and the needs of AI-integrated operations. Also not something we want to hear in Africa.
- Data security: The vast amount of data collected by AI systems can be vulnerable to breaches, risking the exposure of sensitive information about plant operations and employee activities.
- Surveillance: Increased use of AI in security and monitoring can lead to enhanced surveillance, raising ethical concerns about privacy and worker autonomy.
- Decision-making authority: The delegation of decision-making to AI systems raises ethical questions about accountability and the appropriate level of human oversight.
- Impact on quality of life: While AI can improve efficiency and safety, it can also contribute to a high-stress environment if not implemented thoughtfully, particularly if it leads to job insecurity or increased surveillance.
- Broader societal impacts: There are also broader societal impacts to consider such as potential long-term dependence on technology, reduced human expertise, stifling of innovation and flexibility, and significant shifts in workforce requirements.
Operational risks
- System failures: Dependence on AI systems increases the risk of significant disruptions if the AI system fails or malfunctions. Redundant systems and fail-safes are critical to mitigate this risk.
- Cybersecurity threats: AI systems can be targets for cyber-attacks, potentially leading to data theft, operational disruptions, or safety incidents.
- Integration challenges: Integrating AI into existing BMS infrastructure can be complex and costly, with risks of operational disruptions during the transition period.
- Interoperability issues: Ensuring that AI systems work seamlessly with various components of the BMS and other plant systems is critical to avoid inefficiencies or failures.
- Dependence on data quality: AI systems require high-quality, accurate data to function effectively. Poor data quality can lead to incorrect predictions or decisions.
- Continuous monitoring and updating: AI systems need regular updates and monitoring to ensure that they adapt to new conditions and continue functioning correctly. Neglecting this can result in outdated or ineffective AI models.
- Integration with legacy systems: Many existing HVAC systems may not be compatible with AI technologies, requiring significant upgrades or replacements.
- Skill requirements: The integration of AI into HVAC design and operation requires new skills in data science, machine learning, and software management for engineers and technicians. Regulatory compliance
- Validation and oversight: Ensuring that AI systems comply with stringent regulatory requirements is complex. Failure to do so can result in non-compliance penalties, recalls, or other regulatory actions.
- Bias and transparency: AI decision-making processes must be transparent and free from bias. In the pharmaceutical industry, biased decisions can lead to product quality issues or unjustified deviations from standard procedures.
b. Mitigation strategies
- Human-in-the-loop systems: Ensuring human oversight, decision making and intervention capabilities.
- Robust cybersecurity measures: Implementing strong cybersecurity protocols to protect against breaches and cyber-attacks.
- Regulatory collaboration: Working closely with regulatory bodies to ensure compliance and ethical standards.
- Continuous training: Providing ongoing training for employees to adapt to new AI systems and technologies.
AI and fully integrated smart building technologies are transforming BMS in pharmaceutical plants by enhancing design, operational efficiency, safety, compliance, and environmental sustainability. These systems leverage AI, IoT, and advanced automation to create not only more efficient but also more sustainable and eco-friendly environments.
While offering significant benefits, these technologies also present challenges that require careful management and mitigation strategies.
