At New Murabba, we set new benchmarks by introducing the latest methods and technologies into District Cooling systems, leaving behind conventional practices. Legacy chillers’ configurations shall be replaced and automation of controls through AI shall be introduced, as well as optimised sequences of operations.

Our goal is to ensure a complete integration of AI algorithms within the Automation Control Framework and to set new benchmarks. The performance readjustment of various active assets will be done on a continuous basis, enabled through real-time data management and deep understanding of interdependencies of the same.

This marks the beginning of a huge digital transformation in the District Cooling industry, whereby AI algorithms and predictive models take over tasks hitherto being performed by human beings, such as data analysis, predictive maintenance and making setpoint adjustments in real-time. These systems will continuously learn and keep improving with volumes of data emanating from a multitude of IoT sources and, hence, facilitate never-before-achieved optimisation.

Key features of the AI-driven system

It all starts with seamless, secure connectivity across all assets to enable frictionless data flow from all IoT devices to the data lake. The raw data gets cleaned, sorted and analysed. AI algorithms will probe this curated data, extracting actionable insights from various sources, particularly historical BMS outputs, occupancy trends and weather conditions. The algorithm will recommend the optimal cooling demand and set the best-in-class operational parameters.

The subsequent layer of the application lies in suggestions for improvements due to the aggregation of data from various sources, redefining how these sequences of operation are defined based on what really matters to operators. It uses AI models to predict system degradation, asset failures and deviation from the design intent to improve overall plant performance. Real-time setpoint management allows the operators to take necessary actions upfront, thus really allowing for the possibility of remote resolution through centralised control.

Looking ahead to autonomous District Cooling systems

At the New Murabba, we believe the District Cooling industry has now entered the new era of efficacy, where AI and advanced analytics self-manage, adjust and optimise systems in real time. Be it predictive maintenance, AI-driven energy optimisation or weather forecasting by predicting their behavior and adapting to peaks and troughs, these systems are incessantly upscaling energy efficiency, therefore very much going towards autonomous plants.

Key trends powering AI adoption

A few factors have accelerated the pace of integrating AI into District Cooling operations, beyond imagination. These factors are:

· The availability and economy of IoT sensors: Reflecting real-time data on systems’ performance and environmental conditions allowing decisions commensurate with accuracy and timing.

· Advances in cloud computing: The scalability of cloud platforms allows enormous data storage and processing power with remote monitoring, thereby enhancing the functionalities of AI-driven systems.

· Relatively low computing power and inexpensive deployment costs make possible the implementation of expensive advanced AI and ML algorithms.

· Open automation technologies make system integrations and control strategies less problematic, hence making AI more accessible.

· Generative AI: It will enable advanced system optimisation by learning the patterns within operational data.

· Seamless integration: AI-driven insights in every step of the cooling process – design, construction, operation and maintenance – lead to faster preventive and predictive reactions.

Challenges and strategic solutions

With some key benefits, some challenges must be overcome to realise the fullest potential of high-performance operations. Some major concerns include Low Delta T Syndrome, idle capacity and heat recovery opportunities. How AI and other technologies can help in the given scenarii are as follows:

Low Delta T Syndrome: As is reasonably well known, the is the condition when there is a lack of appropriate temperature difference between the chilled water supply and return lines due to inefficiency. This inefficiency forces chillers, pumps and cooling towers to work harder, consuming more energy and increasing operational costs. Addressing this critical issue requires a best-in-class design practice to ensure the following:

Correct sizing of cooling components, such as chillers, plate and frame heat exchangers, pipes, decouplers and valves, to optimise the cooling plant performance.

Variable primary flow mechanism: This mechanism will ensure the dynamic variation in the chilled water stream to maintain the best setpoint temperature.

Leveraging advanced controls and IoT sensors with AI-driven algorithms to monitor and adjust operational control points in real-time is a feature. PICVs on coils allow for pressure-independent control to achieve exact water flow, with minimal wastage, further optimising energy efficiency.

Idle Capacity: The system, if producing more chilled water than is used, can be accurately predicted by employing the power of AI, using historical data on weather patterns and occupancy. Thermal Energy Storage (TES) systems can play an important role in mitigating this issue by allowing surplus chilled water to be stored during low-demand periods and be used during peak times, to be able to balance production with actual demand.

Integration of cooling for data centres: Data centres need to be integrated with the District Cooling loop, requiring low grades of chilled water. This will enable data centres to make use of the return chilled water line, enhance the system efficiency and reduce the cost of cooling.

Use of Treated Sewage Effluent: The use of treated sewage effluent for cooling applications will reduce dependence on freshwater, mainly in cities like Riyadh. On-site STPs will treat the water, which will be a sustainable and economically viable source of water.

Waste Heat Recovery: The employment of heat recovery systems traps this waste heat and reduces the heat rejection loads on the cooling towers. That could be utilised to preheat domestic hot water or for heating swimming pools. Booster heat pumps may be used to maximise the amount of recovered heat while keeping the water at a safe temperature.

Renewable Energy Integration: Integrating PV and thermal solar panels, coupled with heat recovery system, will allow the improvement of efficiencies and reduce dependence on conventional sources of power, which enhances the project’s sustainability.

Delivering optimum efficiency and sustainability

Success will come from how far AI-driven technologies are brought in and how well operators manage the performance at cooling plants. With predictive models, real-time data and renewable energy sources, operators will be able to bring down costs, enhance system performance, and alleviate the financial loads associated with developers and end users.

Conclusion

The future of District Cooling will be shaped only by AI-powered autonomous systems that adapt and improve continuously with real-time data and predictive analytics. In this regard, for fast-tracking at every level, industry, researchers and policy framers need to join hands and ensure this happens with due regard to data privacy concerns, reskilling of human resources and the formulation of new standards for infrastructure driven by AI. Finally, with strategic partnerships and further innovations, AI-enabled cooling systems will be found in the heart of sustainable, energy-efficient infrastructure in the years to come

The writer is Senior Director, Design Management, MEP at New Murabba. He may be contacted at snezar@newmurabba.com