What challenges is the downstream sector facing?
One of the biggest challenges in downstream is incomplete visibility of refinery or terminal operations. Operation managers lack critical intelligence about their equipment and processes. For example, how are assets such as heaters, mixers, or pumps being utilised, and when are they needed? Are they functioning as expected, or has performance declined and should proactive maintenance be performed? What are the energy costs associated with specific activities (processes) and what are my best sources of energy that minimises costs and environmental impact?

Another challenge is the complexity of supply chain logistics. Crude procurement, inventory, and fuel distribution decisions are often manually planned, based on phone calls or manually entered data. These processes are prone to human errors and do not have the ability for optimisation. With low margins in this sector, efficiency and optimisation based on real-time operations information is critical.

And in loading/unloading terminals, scheduling, allocation and coordination of bay configuration and fleet (fuel distribution) is often subject to disruption or change. With so many variables that are constantly changing (e.g. truck arrivals, product requirements, available inventory, loading bay queues), it is a super-human task to manage operations in this environment while maximizing terminal throughput.

How do you see digitisation and disruptive technologies changing the downstream sector?
Digitisation and the benefits of software IIoT platforms brings speed, service, and (cost) savings to the downstream sector. Edge computing brings speed to gaining real-time operational visibility in downstream operations. Access to this real-time data and information can impact operations decisions immediately. For example, in a refinery individual steam generation equipment can be profiled for output rate, real-time information from weather condition, production rate, and system parameters can be extracted to predict steam demand, and operations managers can leverage this predictive intelligence gained through machine learning to make proactive energy decisions.

By leveraging software to simplify the complexity of supply chain logistics, better service can be provided to retail customers. Coordination across supply chain logistics for terminal fuel delivery – what products are needed where, and by when, offers optimised inventory management. By digitising the supply chain, insights can be gained from across all terminals, leading to better forecasting of demand and supply planning across the entire commercial operation.

Savings in costs are critical in downstream operations and leveraging Industrial IoT software with artificial intelligence means critical costs such as energy usage can be better managed. Through machine learning and predictive analytics, individual equipment energy costs and processes can not only be understood, but then optimized.

How willing is the sector to accept change and new technologies?
Our downstream customers are very willing to understand, evaluate and adopt these new technologies. They are excited about the opportunities of bringing tangible ROI, such as reduction of energy costs and increasing efficiency in loading terminals, to the business. Some are already scaling their deployments to multiple applications or several sites, others are starting to invest in high-value pilots. All in all, almost every company is educating themselves by evaluating a range of IoT solutions that can bring lasting impact to their business.

What innovations in products and/or services do you have in the market or are working on?
Digital Terminal - IoT solution for terminals. This end-to-end solution Integrates terminal equipment and systems, including meters, gauges, or any other sensors, connecting all vendor hardware like pumps and relays, into a single operating platform, providing granular visibility at individual terminals and collective intelligence across terminals. With machine learning, the software performs intelligent predictions on equipment performance, energy use and loading times, empowering terminal operators to reduces terminal operations costs, simplifies logistics and optimises supply chain decisions.

Loading Optimisation – AI for terminal loading. This solution integrates truck loading schedules and terminal systems to generate predictive queuing strategies that can improve loading productivity by 15%. At the terminal, the software extracts product inventory and loading bay information. AI matches trucks to loading bays and finds the most efficient terminal loading system configuration based on predicted loading demand flows.

Energy Efficiency – Predictive analytics for refinery energy use. This solution turns the traditional energy optimization approach upside down. Instead of examining energy use from the rear-view mirror, the software profiles and predicts energy demand for individual processes and equipment in real time. Whether it is steam or electricity, the process energy and machine drive energy are profiled with predictive analytics. Energy generation and sourcing decisions are accordingly optimised to create savings, based on the intelligence.

Can you tell us about some of your work in this sector?
Downstream customers have deployed the Atomiton Digital Terminal solution to optimize their energy usage. As energy is one of the main resources in refining and processing, having visibility and control over these costs provides significant ROI. Peak energy use has been challenging, as it impacts future energy costs based on higher demand costs and may have negative environmental impact (e.g. excess steam). Before deploying the solution, it was difficult to attribute energy costs to various assets or processing activities. Combined with the operational constraints (e.g. specific products that needed to be produced in certain timeframe), manually managing and reducing these energy costs was too difficult.

With the Atomiton solution, assets and activities (processes – e.g. distilling, cracking, mixing) are digitised, and energy use can be monitored and profiled. Predictions of energy load based on operational plans and schedules can be made. Activity schedules can be optimised against existing operational constraints, to reduce peak and overall energy usage, and thus reduce energy costs.
 

Jane Ren will be talking about the future and emerging tech trends at Future Downstream conference on 4 December, London.