Digital Feature: Delivering the next-generation workforce by harnessing AI to propel chemical engineering into the future
G. COHEN, CEO and Co-Founder, Imubit, Houston, Texas
The petrochemical industry faces a significant shortage of skilled workers, a challenge exacerbated by an aging workforce and a need for more new talent entering the field. According to a 2023 report by the American Chemistry Council, nearly 40% of the workforce is expected to retire within the next decade, intensifying the skills gap. Additionally, a survey conducted by Deloitte in 2022 highlighted that 70% of petrochemical companies need help finding qualified candidates for key roles. This shortage threatens to reduce competitiveness and slow down industry growth and innovation, making investing in workforce development and training in new technologies critical.
The convergence of an aging workforce, a shrinking talent pool interested in manufacturing careers, and the increasing complexity of industrial operations has created an urgent demand for skilled professionals who can navigate the challenges of today while preparing for the demands of tomorrow. The industry’s reliance on traditional methods is being outpaced by the rapid advancements in artificial intelligence (AI) and automation technology, which are reshaping every aspect of plant operations, from routine maintenance to strategic decision-making.
It has been accepted that AI can help address that shortage of skilled workers by automating routine tasks, optimizing operations, and enhancing training programs through virtual simulations. AI-driven predictive maintenance has been proven to reduce downtime, allowing the existing workforce to focus on more complex, high-value tasks, increasing overall efficiency.
However, adopting AI within the petrochemical sector, where safety and environmental compliance are critical, comes with concerns. As AI advances at an unprecedented pace, industries worldwide face a pivotal moment of transformation. Integrating AI-driven technologies promises unparalleled efficiency, innovation, and productivity but also stirs significant concerns among workers. Fears that AI might replace human labor are not unfounded; these worries are rooted in observable trends where automation has already displaced jobs in manufacturing, logistics, and other sectors.
However, the conversation extends beyond mere job loss. It is about the future of work itself, the redefinition of roles, and the evolving relationship between humans and machines. As we navigate this technological revolution, it is crucial to address these concerns, exploring how industries can harness AI to complement rather than replace human skills and how societies can prepare for a future where AI and human labor coexist harmoniously.
Much of the commentary in both mainstream and specialist media surrounding the impact of AI on workers falls short of the mark. AI is not going to take your job away, but a professional skilled in AI will. It is, therefore, critical that workers can harness the power and benefits of AI without requiring programming skills.
Deploying AI to empower rather than replace workers. Democratizing AI is crucial to empowering workers and ensuring that advanced technologies are accessible and understandable to all, not just the data science savvy. By making AI tools user-friendly, explainable, and transparent, employees across all levels can harness its potential to enhance productivity and innovate. This approach fosters a more inclusive work environment where everyone can contribute to and benefit from AI-driven advancements, ultimately driving collective success and growth.
This is the strategy behind the author's company's industrial AI platforma, which empowers chemical industry SMEs like process operations, controls, engineering, and economics – a workforce that may not have traditional programming skills – to work with cutting edge AI algorithms. The AI platforma is an AI process optimization solution that continuously executes the plant’s key strategies in closed loop, utilizing the author's company's deep-learning process controlb. It transforms the workforce by making AI accessible to all technical staff and fosters seamless One-Model Collaboration, ensuring measurable, sustained value generation for years to come.
Traditional linear optimization methods in plant operations have almost reached their limits in delivering the competitive edge necessary for today's dynamic industrial landscape. As process complexity increases, AI-driven optimization solutions offer a transformative opportunity. However, in a market saturated with options, focusing on strategies that deliver measurable results and elevate operational performance is crucial.
For decades, linear and first-principles models have been the backbone of plant optimization, driving incremental improvements. Recently, hybrid models integrating AI and machine learning with first principles have gained traction. Yet, these models often need to be more open to their creators’ understanding, leading to more opinion-based rather than data-driven collaboration. This siloed approach limits the potential for truly integrated, evidence-based optimization across the entire plant.
The advent of true AI in closed loop systems marks a paradigm shift. Unlike traditional methods, true AI adopts a data-first approach, constructing models based on historical data enriched with domain expertise. This approach begins by developing a neural network model that reflects plant operations, using years of accumulated data and expert insights as its foundation. Offline reinforcement learning (RL) then simulates millions of operational scenarios, effectively compressing decades of plant experience into the model. The result is a powerful AI system that optimizes operational efficiency by maximizing yields and reducing emissions. It also enhances collaboration by providing a model that is accessible and understandable to all stakeholders.
This AI-driven approach mirrors the human brain’s cognitive processes, synthesizing sensory input and experience to create a cohesive perception of reality. Similarly, in an industrial setting, data from various disciplines, such as planning and economics, process engineering, and operations, are integrated into a single, unified model. This model optimizes plant processes, and fosters enhanced collaboration among different teams, making the decision-making process transparent and understandable to all.
The author's company's AI platforma approach is a streamlined strategy for closed-loop optimization. Unlike conventional methods where each group operates its own model, the author's company's AI platforma advocates for a singular, unified model that reflects the collective understanding of all groups involved in the plant’s optimization strategy. This approach is grounded in data, ensuring decisions are based on evidence rather than opinions.
Before implementing closed-loop control, users need to understand the decision-making process of the AI platforma. This platform simplifies this process by providing applications that allow engineers, operators, controls teams and economists to explore the relationships between their independent and dependent variables. By focusing on process constraints, objectives, and economic parameters, users are empowered to analyze, adjust and observe model response, gaining valuable insights into optimal production moves. This builds trust in the AI-driven decisions, paving the way for a new era of industrial excellence.
Future proofing chemical engineers. Chemical engineering has long been a lucrative field, attracting top talent with its promise of rewarding careers. Today, as a new generation of workers enters the workforce, they bring with them not only chemical engineering expertise but also a strong foundation in data analytics. These young professionals are acutely aware of the rapid technological shifts shaping the world and are eager to participate in this revolution. The author's company empowers them to participate in and harness the potential of RL within closed-loop systems.
Even in some of the most technologically advanced petrochemical companies, this next generation of digitally savvy chemical engineers is asking for more. They want access to cutting edge AI technologies. Honing their skills around these technologies will not only future-proof their careers, but also increase loyalty to companies providing these opportunities. By offering access to cutting-edge AI tools, the author's company not only future-proofs their careers but also enhances their commitment and loyalty to their companies.
The integration of AI in the petrochemical industry presents a transformative opportunity to address the pressing challenges of an ageing workforce and a skills gap. By democratizing AI and making advanced technologies accessible to all, companies are empowering workers to leverage AI's potential without needing specialized programming skills. The Optimizing Brain exemplifies how AI can enhance collaboration, optimize plant operations, and drive innovation, ultimately ensuring that the next generation of chemical engineers is not only equipped to meet the demands of the future but also poised to lead the industry into a new technology era.
NOTE
a Optimizing BrainTM
b Deep Learning Process Control®
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