A highly‬‭ accomplished‬‭ Senior‬‭ Water/‬‭ Wastewater‬‭ Process‬‭ Engineer‬‭, Michael Ayorinde Dada has a proven‬‭ track‬‭ record‬‭ of‬ implementing‬‭ strategic‬‭ process‬‭ engineering‬‭ initiatives‬‭ resulting‬‭ in‬‭ significant‬‭ cost‬‭ savings,‬ increased‬‭ efficiency,‬‭ and‬‭ improved‬‭ product quality. In this interview, he speaks on revolutionising water management with AI and Adaptive Simulation.

Your project on developing an adaptive, multi-scale simulation model has been described as transformative. Could you provide a deeper dive into the scope and goals of this initiative?
Certainly. This project is born from the pressing need to address two interconnected global challenges: water scarcity and pollution. Both issues have far-reaching implications, especially in underserved and vulnerable communities where access to clean water is limited, and pollution disproportionately affects livelihoods. The adaptive, multi-scale simulation model integrates advanced computational simulations with artificial intelligence (AI) to create a system capable of optimizing water treatment processes across diverse scenarios and scales. The overarching goal is not just incremental improvement but a paradigm shift in water reuse and resource recovery. We aim to enhance water recovery efficiency by up to 40%, reduce energy consumption by 20–30%, and cut operational costs, making advanced water treatment accessible even in resource-constrained environments.

Can you share some insights into the current inefficiencies in water treatment that your model seeks to address?
Of course. Traditional water treatment systems, while effective in their design era, often struggle with inefficiencies due to aging infrastructure, rigid processes, and the lack of integration with modern technology. For example, it’s estimated that over 30% of treated water is lost to leaks or evaporation in municipal systems, and energy costs can constitute up to 60% of a facility’s operational expenses. Additionally, treatment processes are typically static, unable to adapt to changes in water quality, volume, or contaminants in real-time. These limitations result in underutilized resources and increased costs. Our model addresses these pain points by creating a system that is not only more efficient but also adaptable to dynamic conditions, enabling consistent performance and reducing waste.

You mentioned adaptability. How does your model scale to meet the needs of both underserved communities and large urban centers?
Scalability is integral to the model’s design. For underserved rural areas, the model can operate on compact systems with minimal energy requirements, leveraging renewable energy sources like solar power. For instance, in communities with fewer than 10,000 residents, the system can optimize modular treatment plants, recovering up to 90% of water from wastewater at significantly lower costs. In contrast, for urban centers with complex water networks, the model integrates seamlessly with existing infrastructure, managing high treatment volumes and ensuring peak efficiency even under fluctuating demand. The system’s multi-scale capability means it can cater to a wide spectrum of needs, from decentralized small-scale operations to centralized municipal systems.

How do computational simulations and AI technologies enhance this adaptability and efficiency?
Computational simulations allow us to model the physical, chemical, and biological processes involved in water treatment in extraordinary detail. These simulations enable us to identify bottlenecks, inefficiencies, and areas for optimization under various scenarios. AI takes this a step further by analyzing the data generated by these simulations and operational systems. The AI algorithms learn from historical and real-time data, predicting system behavior, recommending process adjustments, and even preempting equipment failures. For example, if sensors detect a rise in contaminant levels, the AI can adjust filtration rates or chemical dosing immediately, ensuring compliance with water quality standards. This dynamic response capability minimizes downtime, reduces energy use, and ensures consistent output quality.

Let’s discuss the data side. What kinds of datasets feed into your model, and how do you ensure their accuracy and reliability?
The model incorporates a diverse array of datasets, including water quality metrics like pH, turbidity, and contaminant levels, operational parameters such as flow rates and pump efficiency, and external environmental factors like temperature and rainfall. Data accuracy is critical, especially when the system relies on real-time inputs for decision-making. To ensure reliability, we use high-precision sensors for data collection and implement stringent validation protocols. Additionally, the AI algorithms monitor incoming data continuously, identifying anomalies and flagging potential issues for review. In regions with limited data infrastructure, we collaborate with local partners to establish baseline datasets and provide the tools needed for ongoing monitoring and improvement.

Beyond operational efficiency, your project emphasizes sustainability and resource recovery. Could you elaborate on these aspects?
Sustainability is at the heart of this initiative. By optimizing water reuse, we reduce dependency on freshwater sources, alleviating the strain on ecosystems and preserving biodiversity. Resource recovery is another critical component. For instance, nutrients like nitrogen and phosphorus can be extracted from wastewater and used as agricultural fertilizers, creating additional value for treatment facilities. Similarly, biogas generated during treatment can be harnessed for energy, powering operations or even feeding surplus energy into local grids. These processes align with circular economy principles, ensuring that waste becomes a resource, reducing environmental impact while also offering economic benefits.

That’s remarkable. Have you conducted any pilot studies to test the model, and if so, what were the outcomes?
Yes, we recently completed a pilot study in a rural community with approximately 5,000 residents. The results were beyond encouraging. We achieved a water recovery rate of 92%, up from the baseline of 60%, while reducing energy usage by 28%. Operational costs for the treatment plant decreased by 35%, largely due to the optimization of chemical dosing and energy consumption. These results have provided a strong proof of concept, and we are now in the process of scaling up to larger pilot programs in urban and semi-urban settings. These next steps will allow us to validate the model’s performance under more complex and variable conditions.

What are some of the challenges you’ve faced in developing and implementing this model?
The challenges are both technical and logistical. From a technical standpoint, integrating diverse datasets and ensuring the accuracy and reliability of simulations across various scales is no small feat. Logistically, the deployment of such advanced technology in underserved areas often encounters infrastructure limitations and resource constraints. Another challenge is building local capacity to operate and maintain these systems effectively. However, these challenges have also driven innovation. For example, we’ve developed simplified interfaces and training programs to ensure that local operators can manage the systems without needing extensive technical expertise.

What do you see as the long-term ecological and social impacts of deploying this model on a global scale?
The ecological benefits are significant. By enabling higher rates of water reuse, we can drastically reduce the extraction of freshwater, preserving aquifers, rivers, and lakes. This contributes to ecosystem restoration and biodiversity conservation. Socially, the model has the potential to revolutionize access to clean water in underserved regions, improving public health and economic stability. Imagine a future where even the most remote communities have access to reliable, affordable water treatment systems—this model makes that vision achievable.

Looking ahead, what are the next steps for the project, and how do you see it evolving over the next decade?
The immediate next step is to expand pilot testing to larger, more diverse environments, including urban centers and industrial applications. Over the next decade, I envision the model becoming a global benchmark for adaptive water management. We plan to collaborate with international organizations to integrate this technology into global sustainability initiatives. The long-term goal is not just to refine the model but to make it accessible to all, ensuring its benefits are felt where they are needed most.