5 Technologies Transforming Chemical Manufacturing

The chemical industry has experienced significant changes in recent years, with the emergence of substantial challenges. The energy crisis in Europe, fluctuating raw material prices, supply chain issues, the Covid-19 pandemic, workforce constraints and changing market demands have made things difficult for chemical manufacturers.  

However, successful chemical manufacturers understand that new technologies can help them address many of these challenges. They embrace these advancements, knowing that they can revolutionise their operations, achieve more sustainable results and drive industry progress.  
 
In recent news, Artificial Intelligence (AI) continues to stand out as a leading digital technology. As we recognise the transformative impact of advanced analytics and AI software solutions on chemical manufacturing, this blog post delves into the exciting realm of innovative technologies that, when paired with AI, can unlock the potential for operational excellence and offer a significant edge in the competitive landscape. 

Here are five technologies that can empower chemical manufacturers and take their business to new levels: 

1. Industrial Ops

A new software category that is key to helping companies adopt Industry 4.0 is Industrial DataOps. Processing data through layers of systems worked for many years primarily because the amount of data was relatively limited. This is no longer the case. Pushing excess, unused data through systems that do not need it (to arrive at those that do) complicates and slows processing, reduces security, and increases vulnerability. 

Known as Industrial DataOps, this technology provides data standardisation and contextualisation, solving data architecture and integration challenges for enterprise-wide use. 

An Industrial DataOps solution usually includes five capabilities to achieve value: 

• Data Transformation: Manufacturing data from various controllers and machines must be analysed and correlated to derive insights. Standard models within a DataOps solution handle the scale and present the data to IT applications. 
• OT and IT Integration: DataOps solutions must seamlessly connect with industrial devices/systems (with proprietary protocols) and IT systems (using APIs and integrations) to ensure compatibility and value across operations and business applications. 
• Information Management: Data flows must be controlled and managed within a system, allowing for identification, enablement, disabling, and modification of data. The operations team needs control over data flow and conditions. 
• Scale and Security: Industrial data is vast and requires industrial-level scale and security. It must be contextualised, delivered quickly, and secured as it contains valuable intellectual property. 
• Edge Computing: Industrial DataOps solutions should be located close to the data source for efficient processing. They need to share models for data governance and enable analytics and visualisation applications at the edge. 

HighByte Intelligence Hub is an example of an Industrial DataOps solution, designed specifically for industrial data modelling, delivery and governance. It enables manufacturers to securely connect, model, condition, and flow industrial data to and from IT systems without writing or maintaining code. The software is deployed at the Edge to merge real-time, transactional, and time-series data into a single payload for consuming applications. With the Intelligence Hub, users can speed system integration time, rapidly leverage contextualised data for analytics, artificial intelligence (AI), and machine learning (ML) applications, and govern data standards across the enterprise. 

2. Time Series Database 

A time series database (TSDB) is optimised for time-stamped or time series data. Time series databases are not new, but the first-generation time series databases were primarily focused on looking at financial data. But nowadays everything has, or will have, a sensor. So now, everything inside and outside the company is emitting a relentless stream of metrics and events or time series data. This means that the underlying platforms need to evolve to support these new workloads — more data points, data sources, monitoring, and controls. 

A TSDB is optimised for measuring change over time. Properties that make time series data very different from other data workloads are data lifecycle management, summarisation, and large-range scans of many records.  

InfluxDB is built for developers and is central to many manufacturing solutions providing high throughput ingestion, compression, and real-time querying of industrial sensor data. Manufacturing, automation, system, and process engineers use InfluxDB to collect IIoT sensor data used to reduce machine downtime, improve manufacturing output and improve plant safety and predictive maintenance strategies. InfluxDB is a central platform where all IIoT metric data can be integrated and centrally monitored. 

3. Connected Workforce Platform  

While many manufacturers have made investments in automating their plants and resources and implementing new technologies, they still encounter three primary challenges:  

• Manual processes: Daily operations often rely on paper manuals and manual data recording for executing standard operating procedures. This approach leads to operational inefficiencies, potential human errors, and difficulty accessing and retrieving data. 
• Information silos: Processes are not standardised or effectively captured, posing challenges in maintaining compliance and driving operational improvements. This lack of standardisation impedes the seamless flow of information and can be felt as a lack of transparency and collaboration across the organisation. It also brings frustration caused by delays in accessing and analysing data fast enough. 
• Skilled workforce shortage: The manufacturing workforce is undergoing a transition, with experienced workers retiring and young workers less willing to work in the industry. Concurrently, the Industry 4.0 transformation has amplified the demand for digital skill sets, making the challenge even bigger. By making the shop floor user experience more digital and integrated, it is more attractive to younger generations. 

To address these challenges and establish flexible and resilient production environments, connected worker platforms have arisen to leverage the potential of this technology to provide unprecedented insights, enhanced communication and optimised processes for operations staff. Going digital connects people, processes, and technology, empowering the frontline workforce to interact with their environment in productive, smarter, and more efficient ways.  

The Cyzag platform is an example of this technology, integrating real-time data, processes, tools, and analytics, in a collaboration environment, serving as a central hub for manufacturing. This integration empowers plant staff, especially shop floor workers, to make faster, more informed decisions. Its user-centric design enhances adoption and engagement through an intuitive user experience. It offers flexible, easy-to-configure boards and workflows for diverse use cases, balancing standardisation and flexibility. 

An extra advantage of Cyzag’s connected work platform is that it is a no-code, cloud-based platform, enabling users to configure the platform themselves through graphical user interfaces and configuration instead of traditional computer programming. And as a cloud application, it allows users to save data and files in an off-site location that is accessible via the public internet or a dedicated private network connection. 

Manufacturing company Nobian successfully adopted Cyzag’s platform. As a result, they empowered over 550 workers to make real-time decisions in months, contributing to the sustainability of the organisation and avoiding losses of €1 million a year for just one user.  

4. Predictive Maintenance Software

Predictive maintenance software utilises data analytics, machine learning, and artificial intelligence (AI) algorithms to predict equipment failures or maintenance needs before they occur. The software can identify patterns and indicators of potential failures by monitoring and analysing real-time data from sensors, historical maintenance records, and other sources. This allows chemical manufacturers to schedule maintenance activities proactively, avoid unplanned downtime, optimise maintenance costs, and extend the lifespan of critical equipment. Predictive maintenance software helps improve overall equipment effectiveness (OEE) and enhances operational efficiency in chemical manufacturing plants. 

Senseye Predictive Maintenance solution from Siemens enables asset intelligence across plants without the need for manual analysis. It helps manufacturers increase productivity, work more sustainably, and accelerate digital transformation across their entire organisation. Combining AI with human insights, the platform automatically generates machine and maintainer behaviour models to help direct attention and expertise to where it’s needed most. It also integrates with any asset, system, or data source, using data you already collect or with newly installed sensors as part of a complete package. 

5. Digital Twins 

Digital Twins are virtual replicas of physical assets or processes, such as chemical plants, reactors, or equipment. They integrate real-time data from sensors and other sources to create a digital representation that mirrors the behaviour and performance of the physical counterpart. In chemical manufacturing, Digital twins provide a deeper level of understanding, monitoring, and predictive capabilities that can enhance decision-making, maintenance strategies, and overall operational efficiency. Operators or users can use the twin to test scenarios before implementing changes in real scale.  

While process simulation software focuses primarily on understanding and optimising chemical processes, digital twins extend their capabilities by incorporating real-time data and creating a dynamic, virtual replica of the physical process.  

TwinThread is a cloud-native platform combining Digital Twins with groundbreaking AI, to identify, prioritise and execute continuous improvement initiatives for process and asset quality, throughput, reliability, and sustainability. It automatically discovers data points across your network and gathers data, from smart devices to data historians. As the data is collected and aggregated, an exact virtual representation of your operating environment is created. Once the twin is created, the library of pre-built analysis applications delivers increasingly insightful and granular views of your automation environment, offering specific and detailed corrective actions aimed at improving efficiency. 

Summary 

The integration of new technologies in manufacturing creates a pathway for collaborative and agile production systems. Adopting and integrating new technologies enable the chemical industry to enhance productivity and sustainability, improve efficiency, reduce costs, and accelerate innovation. 

The successful incorporation of new technologies requires a proactive mindset, investment in research and development, and a willingness to adapt to change. As manufacturers embrace these new possibilities, they are well-positioned to reap the immense benefits and positive impacts that come with mixing new technologies. 

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