Major opportunities for digital disruption: exclusive interview with Ron Beck, Aspen Technology
Please introduce yourself and your role at AspenTech.
I am the Director of Marketing Strategy. In my 14-plus years at AspenTech, I have been in various industry and product marketing roles. I am also a resident blogger and have written several whitepapers and taken on the role as a spokesperson on topics – such as sustainability, digital transformation, and process optimization.
Prior to AspenTech, I had held similar roles at other technology companies, including a strategic consulting role for several of the world’s largest process manufacturers, such as Dow Chemical, Celanese, AkzoNobel, Duke Energy, Israel Electric and Shell. I also have more than three decades of experience in providing software solutions to the process industries, including 15 years of experience in chemical engineering technology commercialization.
I have authored papers on key industry topics and presented at global industry events, having graduated with a bachelor’s degree from Princeton University in New Jersey.
What are the major opportunities for digital disruption in the energy and chemicals industry within Southeast Asia?
Beyond current economic disruptions, general projections show strong GDP growth outcomes across Southeast Asia in the next decade. Our level of engagement with industries and companies in Southeast Asia continues to grow, and the level of technical talent in Malaysia, Thailand, Indonesia, and Vietnam is impressive. While the current climate remains volatile, we are optimistic about regional opportunities and growth.
In general, Southeast Asian process companies have the right leadership and plans in place to adopt AI solutions that create value. For example, the best AI-based solutions are designed to be used by today’s workers, as these solutions employ powerful analytical engines to provide an intuitive workflow and interface to support strategic decision-making by providing agility.
As the industry navigates the energy transition, technology is a key partner for process companies in Southeast Asia. Companies need to make strategic moves to improve agility and competitive positions in the future. Due to the complexity of tradeoffs involved, mountains of data need to be analyzed for insights, which makes Industrial AI critical to achieving sustainability but still maintaining profitability. Companies in the region, who can capitalize on the right technology, will have an immense given advantage in accelerating ahead in this playing field.
What is ‘industrial AI’? Why is this approach unique and how does it work?
AspenTech has pioneered an AI and machine learning approach called Industrial AI – combining basic digital models, which allows oil and chemical assets to operate safely. These three models are known as first-principle models, with advanced analytics models (or AI machine learning), and industry domain expertise.
In doing so, practical solutions can be created to break new ground and model challenging problems, which results in predictive insights, as to what should and will happen next. Intuitive usability makes these advanced solutions accessible to regular industry workers.
In a downstream business, who should be accountable/responsible for the implementation and success of industrial AI in the business?
Having the right core team leadership to lead digital transformation via Industrial AI is critical. Industrial AI needs to be viewed as the intersection between information technology and operations. Operational context is crucial. There is a temptation to “do it yourself,” but that won’t work in the long term in this rapidly changing world where companies need to focus on their core competency.
How important is culture when implementing AI into the business infrastructure? How can downstream business leaders ensure that industrial AI is accessible and understood across all parts of the business?
Culture is mission critical, as leaders need to understand both business and technology aspects to achieve the right outcomes. To ensure success, it is necessary to map a plan for the adoption of AI that resonates with the company’s business strategies, key business challenges and value levers. Industrial AI is a proven value creator, so it is crucial to apply technology in the right areas of the business and get the process started.
How can operators know if the Industrial AI technology can help them achieve better sustainability outcomes?
Industrial AI can help companies be more sustainable by looking at three key areas – resources (energy, water) efficiency; energy transition and decarbonization; as well as the circular economy.
Energy is consumed inefficiently in the conversion of hydrocarbons, synthesis of chemicals and the supply chain. Technology will be key in helping the industry drive towards carbon neutrality. For example, heat integration to reduce the demand side or increase energy efficiency through production efficiency of existing operations. The supply side of energy use can be improved via a digital technology weapon, known as utility supply optimization.
Water is projected to be a resource that is increasingly in short supply. Industrial processes need to be redesigned to be more efficient. For instance, the same process simulation models and utility optimization models that helps companies improve energy use can help in water conservation. AspenTech’s hybrid models, which essentially supercharge simulation models with AI-based machine learning, can design and operate these water conservation approaches economically and effectively.
Natural gas is emerging as an important future energy source. Technology can improve the cost and reliability of natural gas supply. For example, digital twin models and advanced control are critical in minimizing energy usage during LNG processing. Prescriptive maintenance technology can alert operators to conditions that create the risk of degradation. LNG plants can also work towards being Self-Optimizing Plants.
Process modeling technology is critical to bioenergy conversion approaches. The carbon capture industry can capitalize on process modeling to minimize energy usage, understand, and optimize carbon dioxide capture processes.
In a rapidly growing Southeast Asian economy, how can downstream operators leverage advanced digital technologies to stay ahead of the industry curve?
Operators need to select the right partners with the capability and strategy to lock in working solutions and be positioned well for the future. It is important to work with a technology supplier with the right strategy and vision, as well as technology architecture that can adapt rapidly to changes. It is mandatory to be robust, resilient, and agile to be ahead of the curve.
What will the smart plant of the future look like? How will it be influenced by macro trends such as sustainability and environmental impact?
The smart plant, or what AspenTech calls the Self-Optimizing Plant, is our strategy to take customers on a journey to the future intelligent plant, asset or set of assets. Our vision is for this futuristic plan to be self-learning, self-adapting, and self-sustaining.
By self-learning, we mean that the plant learns from each action it takes, while data streams and digital twins provide operational insights. By self-adapting, we mean the plant will continually adjust to changes in the asset condition and external factors to change operational objectives continually. By self-sustaining, we mean the plant will intelligently monitor the health of its equipment, processes, and systems – based on data streams and provide insights. It will then take corrective actions to ensure the integrity of the asset, health of equipment, to avoid or minimize degradation and avoid missing customer targets.
This plant capabilities will be critical to navigate complicated technical, operational, and business tradeoffs required to make energy and chemical assets move towards zero-carbon and reduce levels of waste production and water usage. Most companies in the chemical and energy fields are beginning to set ambitious sustainability targets. This ensures that they can contribute to future sustainability and maintain access to capital.
How will the Self-Optimizing Plant change the future?
The Self-Optimizing Plant will maximize the outcomes for sustainability, as operational goals become autonomous in the relative short term but broader autonomy remains as the longer-term goal. Typically, oil and chemical assets are too complex to run completely autonomously – at least it remains unlikely in the near term. However, the next five to ten years is crucial to be on track in driving towards enabling this smart plant of the future.
How can advanced technologies help achieve sustainability goals in energy related areas?
Energy efficiency can be viewed from both the demand and supply perspectives. How do we reduce energy usage on the demand side? Modeling technology, known as pinch analysis, performs an important optimization task called heat integration. How can a refinery or chemical site be made more efficient by reusing heat and cooling sources optimally? AspenTech has provided an intuitively guided automated approach, called Aspen Activated Energy.
Asian companies, like SCG Thailand and S-Oil Korea have led the way. SCG has reduced over 10% energy use at several sites with this approach.
Energy efficiency can be increased through production efficiency of existing operations. Several digital optimization technologies contribute to increasing the production efficiency of oil and chemical operations. Digital twin monitoring systems, dynamic optimization, and multivariate process control (APC) solutions can collectively save 5 – 15% energy use, as well as reduce carbon emissions by a proportional amount.
Refiners, such as CEPSA, Reliance, BPCL and chemical players, such as Mitsui, are leaders in using this approach.
The supply side of energy use can be improved dynamically by using plant data to select the best utility source for each part of the plant. The digital technology weapon is utility supply optimization. Technology can model the interplay between multiple plants, and multiple utility sources. For example, choosing between a wind energy source, natural gas-based electricity, or diesel combustion at the plant, factoring dollar/carbon cost and reliability. The same models can incorporate choices for minimizing water use.
Companies like BPCL are capitalizing on this approach, Alcoa utilizes this approach to reduce electricity use and cost in aluminum processing. BPCL and HPCL deploy Aspen Utilities software, a planning and optimization model.
How can companies maximize sustainability outcomes in the next phase of digital transformation?
At AspenTech, we work closely with our customers to help accelerate the digital transformations that are necessary to thrive in the environment of the future. We deliver the advanced technology that is integral to sustainability, competitive and innovation strategies that unlocks the potential of new business models.
To do this, we are creating industrial software solutions that span functional silos, powered by Industrial AI. This set of software technologies work together to anticipate future conditions and act, accordingly, adjusting operations within the context of the enterprise. This enables new levels of insight and operational guidance, elevates the scope of agility and automation that is possible and is moving the industry towards the vision of the Self-Optimizing Plant, a facility that can automatically respond to changing conditions during operation.
The seamless integration is made possible with the Self-Optimizing Plant which enables companies to meet economic and sustainability objectives, delivering safety and reliability with highly efficient and agile operations to meet the future challenges of new markets and business models.
As such, accelerating digital transformation helps companies achieve sustainability goals.
As companies chart the path forward to a more flexible and agile future, experts from PTTGC, Aspentech, PETRONAS and Wood share their perspectives on value generation opportunities in the near team. With macro forces like market volatility, energy transition, circular economy, and workforce transitions in play, they will discuss how industry operators can navigate their way and how they see the journey to increasingly autonomous operations at a time of change and uncertainty. Click here to register for the webinar taking place on 3rd March at 10.30 – 11.30am SGT.
Ron has 30 years’ experience in providing software solutions to the process industries and 10 years’ experience in chemical and environmental engineering technology consulting and commercialization. During his 14 years at AspenTech, Ron has held several marketing roles, and meets frequently with global customers. Additionally he speaks at many industry conferences and seminars, and is a frequent blogger.