The petrochemical industry, a cornerstone of modern manufacturing, is at a crossroads. With the increasing complexity of operations and the pressure to remain competitive, the need for advanced analytics has never been more pressing.
Before considering the potential opportunity of advanced analytics, let’s first look into different approaches across industry. Historically, petrochemical companies have faced a tough choice: either train their workforce to become data scientists or rely on a central data science team.
Both approaches, however, have significant drawbacks. The former demands a substantial investment in training and time, while the latter often leads to bottlenecks and a disconnect between data analysis and on-ground application.
As with most industries, the idea of transforming the existing workforce into data scientists is extremely challenging for the petrochemical sector. On the surface, it seems like a forward-thinking strategy to harness the power of advanced complex analytics and place it in the hands of everyone. However, the reality is far more complex.
The existing operational workforce, primarily composed of engineers and operational experts, has a deep understanding of petrochemical processes but often lacks the specialized knowledge required for data science.
Training operational professionals to become proficient in data science is not just about teaching them new software or tools; it involves a fundamental shift in their approach to problem-solving and decision-making. This process is time-consuming and costly, and who will run the plant when the workforce is studying python or writing scripts in R?
Professional programming is a dedicated discipline that goes beyond simply learning a new programming language, what about truth tables, minimum set test cases, boundary testing, Object Oriented development, release management, requirements analysis, error handling, maintenance? Will the workforce also be trained in these areas of programming? Not to mention the specialized fields of mathematics and statistics that encompass data science. In reality, you end up with people patting themselves on the back for taking a long time to write an application from scratch in Python that could easily have been sourced from a COTS with a much lower total cost of ownership.
There is also an extreme risk of overcomplication, with Data science code being thrown at the simplest of tasks. If uncontrolled and untested VBA / VBS in spreadsheets is an issue for your company today, wait until the workforce start developing in Data Science languages as well…
So having established that training the workforce to be amateur data science coders is not a valid approach, there is an obvious argument to have all data science activity performed by a central team of professional Data Science programming experts…
However, whilst establishing a central data science team might seem like a streamlined approach to leverage advanced analytics in the petrochemical industry, this strategy comes with its own set of limitations and constraints. Centralized teams, although skilled in data manipulation and analysis, often lack a deep understanding of the intricacies and nuances of petrochemical operations, equipment and engineering units.
We have had cases where customers have approached IT Vizion to help because their data scientists don’t know the difference between a turbine and a compressor and cannot build the models without studying plant equipment… distinguishing between signal noise and real deviations or process disturbance is also an area where engineering tacit knowledge is required and is beyond many data scientists. In another case we had a customer where the incumbent IT support function was tasked with supporting heat exchanger models, but they didn’t know the different engineering units of measure used in different countries and regions and had not applied the necessary conversions.
This disconnection can result in a misalignment between analytical insights and practical, on-the-ground application. Furthermore, a centralized approach can lead to bottlenecks in decision-making, as the team becomes a single point of dependency for data analysis across various departments.
This structure, while consolidating expertise, risks creating delays and potential inefficiencies, as the team may not always prioritize tasks in alignment with the immediate operational needs of different units within the organization. Of course there are some data scientists with a good engineering background but there simply won’t be enough to sustain a centralized team in every industrial company.
Seeq emerges as a solution that bridges the gap between these two extremes. It is a tool that both engineers and data scientists can use effectively. For daily analytics needs, engineers can utilize Seeq’s intuitive interface, while data scientists can leverage its advanced features for complex analytics tasks. Considering the rapidly evolving nature of generative AI and self service analytics platforms, surely it makes more sense for engineers to focus on their core competence and learn how to leverage the easy to learn and easy to use platforms. This approach is far more feasible and sustainable with the shorter time-to-value.
In the petrochemical industry, beyond health, safety and the environment, operational efficiency is paramount. Advanced analytics, especially through Seeq, plays a crucial role in enhancing this efficiency, this way, Seeq helps in identifying process inefficiencies, predicting maintenance needs, and optimizing resource allocation.
The implementation of advanced analytics through Seeq directly contributes to cost reduction and profit maximization. Using data-driven insights, petrochemical companies can make more informed decisions that lead to cost savings in areas like raw material procurement, energy usage, and waste management.
One of the key advantages of Seeq is its ability to facilitate routine and ad-hoc analytics of real-time data. This capability enables petrochemical companies to shift from a reactive to a proactive approach in their operations. Real-time analytics aid in immediate identification of issues, anomalies, process disturbances, quality and emission events, allowing for prompt intervention and pre-actions that address emerging challenges.
Seeq’s user-friendly interface is specifically designed to cater to the needs of engineers who may not be skilled in traditional data science techniques. This interface allows engineers to easily navigate, analyze, and interpret data without the need for extensive training.
While Seeq is user-friendly for engineers, it also offers advanced capabilities that are vital for data scientists. These capabilities include complex data modeling, predictive analytics, and machine learning algorithms.
One key advantage of Seeq in the petrochemical industry is its seamless integration capability with existing systems. This feature is crucial for companies looking to adopt advanced analytics without overhauling their current technological infrastructure.
The future of the petrochemical industry is increasingly being shaped by the integration of predictive analytics and emerging generative AI, a trend that promises to redefine operational efficiencies and strategic decision-making. Predictive analytics, by harnessing the power of historical data and advanced algorithms, enables companies to forecast future trends, anticipate potential problems, and optimize processes.
In the petrochemical sector, this means being able to predict equipment failures, optimize production schedules, and enhance supply chain management. The ability to foresee and mitigate issues before they escalate can lead to significant cost savings, reduced downtime, and improved safety standards.
Predictive analytics opens the door to innovative approaches in product development and market strategies, allowing companies to stay ahead of industry trends and customer demands. As sustainability and environmental responsibility become increasingly crucial in the petrochemical industry, predictive analytics offers a vital tool in managing these aspects.
This focus on sustainability can also enhance a company’s public image and market position, as consumers and investors alike are increasingly drawn to environmentally responsible practices. In the long term, the integration of predictive analytics is poised to transform the petrochemical industry into a more agile, efficient, and sustainable sector, driving innovation and growth in an era marked by rapid technological advancements and environmental consciousness.
When comparing the petrochemical industry’s use of analytics with other sectors, several unique challenges and opportunities come to light. Industries like finance and retail have been quick to adopt analytics, using data to drive customer insights and optimize supply chains.
The petrochemical industry, dealing with more complex and hazardous materials and processes, has unique safety and regulatory considerations that these other industries do not face. However, it can learn from their rapid integration of data-driven decision-making, especially in areas like demand forecasting, customer relationship management, and lean operations.
The decision to invest in advanced analytics like Seeq is significant for any petrochemical company. Measuring the return on this investment involves assessing both tangible and intangible benefits. Key performance indicators (KPIs) such as reduced operational costs, improved yield, reduced downtime, and increased safety can provide a quantitative measure of success.
However, the qualitative aspects, like improved decision-making capabilities, enhanced employee skills, and a culture of innovation, are equally important. Companies need to establish a comprehensive framework for assessing ROI, taking into account both immediate financial gains and long-term strategic advantages.
As the petrochemical industry embraces advanced analytics, safeguarding sensitive data and ensuring regulatory compliance is paramount. Seeq acknowledges these concerns by incorporating robust security measures and compliance protocols.
This involves encryption of data, adherence to industry-specific regulatory standards, and regular audits.
Companies must also foster a culture of security awareness and compliance, ensuring that all employees understand the importance of data privacy and the legal implications of non-compliance. Best practices include regular training, clear data governance policies, and a proactive approach to managing cybersecurity risks.
The successful adoption of Seeq in the petrochemical industry hinges on effective training and support for the teams using it. This involves not just initial training sessions but also ongoing support to adapt to updates and new features. Companies need to invest in comprehensive training programs that cater to the diverse skill sets of their employees, from engineers to data scientists. IT Vizion is a certified Seeq partner and training provider.
Furthermore, continuous support and knowledge sharing are crucial for solving real-time issues and encouraging innovative uses of the analytics tool. This approach ensures that the workforce is not only proficient in using Seeq but also motivated to leverage its full potential.
The path to integrating advanced analytics in a well-established industry like petrochemicals is often strewn with challenges.
Resistance to change is a common obstacle, as employees may be wary of new technologies and processes. To overcome this, companies must foster a culture that values innovation and continuous improvement.
Technical hurdles can also arise, particularly when integrating new tools like Seeq with existing systems. Addressing these requires a clear implementation strategy, adequate technical support, and possibly partnering with technology experts. By anticipating these challenges and proactively planning for them, companies can ensure a smoother transition to a data-driven operational model.
Incorporating insights from industry experts adds a layer of depth and authority to the discussion on analytics in the petrochemical industry. These experts, with their wealth of experience and knowledge, offer valuable perspectives on the current state and future prospects of analytics in the field.
Their opinions, predictions, and advice can help demystify complex topics, identify emerging trends, and validate the strategic direction of analytics implementation. Including such expert insights in the discourse ensures that the information presented is not only comprehensive but also grounded in real-world experiences and expectations.
From aligning mismatched data to saving energy in massive boiler systems, Seeq is making a real difference. Let’s take a closer look at some of the most exciting and impactful ways Seeq is being used out there in the real world, showcasing just how essential it has become for operational success.
Challenge: Industrial processes often encounter the issue of data misalignment due to time delays in equipment and operations, hindering accurate calculations and correlation analyses.
Solution with Seeq: Seeq’s tools like Value Search, Formula, and Signal from Condition enable teams to align data effectively, translating values to match with other signals and creating a common time basis for data joining.
Impact: This alignment facilitates improved modeling, process adjustments, and enhanced process performance, saving significant time in data preparation.
Challenge: The transition between different products in chemical reactors is a cost-intensive process, leading to the production of off-specification material.
Solution with Seeq: By utilizing Seeq Workbench, SMEs can analyze process and quality data, identifying and calculating the start and end times of product transitions, and thereby optimizing transition schedules.
Impact: This optimization leads to substantial reductions in transition time and off-spec material, boosting profitability and efficiency.
Challenge: Establishing a uniform approach to interpret real-time data in production is challenging with traditional static SPC charts.
Solution with Seeq: Seeq enables the creation of automated SPC control charts, integrating live data and applying relevant limits to specific operating modes.
Impact: Automated control charts enhance decision-making with a robust statistical approach, reducing the time spent on data manipulation and enabling prompt action on production deviations.
Challenge: Manufacturers seek ways to improve sustainability and reduce carbon emissions, with a focus on minimizing energy wastage in utilities like dual boiler systems.
Solution with Seeq: Seeq helps in formulating steam optimization strategies, assessing the financial and environmental impacts of idling a boiler based on historical data analysis.
Impact: This approach has aided companies in saving significant costs on vented steam and contributing to carbon footprint reduction by optimizing boiler operations.
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