Industrial processes are complicated and involve a wide range of factors that must be controlled and improved for effective scaling up from lab to industrial scale. It becomes more complex when non-Newtonian fluids come into the picture. The fluids showcase a behavior that is dependent on shear rate, time, temperature and pressure. Unlike Newtonian fluids, they show a non-linear relationship between shear stress and shear rate which makes it difficult to predict flow behavior at large scale operations. Not only that, with changing shear rate, the viscosity changes and hence transitions between laminar and turbulent flow occurs.
Yeast fermentation is one instance of an industrial process involving a non-Newtonian fluid. Although the microorganism needs an anaerobic pathway to carry out its metabolic processes and produce ethanol, it first needs aerobic conditions for optimum growth in order to ensure high productivity. Due to poor oxygen transfer rates caused by excessive viscosity, the so-called stagnant zones do not receive the oxygen supply that is necessary for the microorganisms to grow to their full potential. Scaling up the procedure consequently becomes extremely laborious and challenging.
Solution by AI
An industrial process, in and of itself, involving complex design considerations and expensive equipment, is determined by a number of characteristics. However, the development of artificial intelligence (AI) has the potential to make major strides in this discipline. Many aspects of industrial operations can be upgraded with the use of AI techniques, such as machine learning, data analysis, and optimization algorithms.
- Data gathering and analysis: Artificial intelligence (AI) algorithms can be used to gather and examine significant amounts of data from industrial processes. The behaviour of the process at a bigger scale can be precisely predicted using this data to create predictive models.
- Algorithms for optimisation: AI can be utilised to create algorithms for optimisation that can find the ideal circumstances for industrial operations on a bigger scale. Finding the ideal temperature, pressure, flow rates, and other process factors might be part of this.
- Predictive maintenance: AI may be used to forecast when industrial equipment needs to be maintained. This can assist reduce downtime and guarantee that the machinery is working well during scale-up.
- Digital twins: Industrial processes can have digital twins made using AI. A digital twin is a virtual representation of the actual process that can be used to model how the process will behave on a larger scale. This can help in predicting and optimizing the process for scale-up.
- Real-time monitoring: AI can be applied to real-time monitoring of industrial processes. This can aid in spotting any problems or deviations from the process‘ expected behaviour and enable fast corrective action.
HSA KIT can improve the analysis process overall in industrial settings by offering specialized solutions, incorporating subjective and objective analysis, standardizing procedures, ensuring consistency and dependability, and facilitating reproducibility. It can help companies analyze their processes more accurately, efficiently, and confidently, which enhances decision-making and overall performance.
Subjective analysis involves personal opinions, values, and interpretations applied to data analysis. Biases, emotions, and other personal elements frequently influence this kind of analysis, which can lead to distinct interpretations and conclusions being drawn from the same data by different analysts.
Contrarily, objective analysis entails using facts and evidence to develop conclusions from data without the interference of subjective viewpoints or prejudices. This kind of analysis uses only observable and verifiable data and follows established standards and procedures to guarantee consistency and repeatability.
An example of detecting yeast and vacuoles
Zoom Out view
While a basic model provides a general or broad overview of the subject matter, giving a high-level understanding without delving into the specific details or variations within the structures; the more customized module takes into account the diverse subtypes within each structure, ensuring that the results obtained are more refined and accurate.
According to the requirements of the client, scalable AI models can be created to support bigger datasets, higher computational demands, or growing user bases. This scalability guarantees that as the client’s business expands, the AI system can handle increasing data quantities and continue to provide accurate and fast results. The models can be designed with a strong focus on privacy and security. Clients can have more control over their data, guaranteeing legal compliance and protecting sensitive data. This degree of personalization aids in building confidence and trust in the AI system.
The model goes beyond basic object recognition by also estimating the average area of the objects detected. By providing information about the average area, it offers additional insights into the spatial distribution and scale of the objects in the scene. Some users might like specific details about each object, like its exact location, size, and class name. Others may place more importance on outputs that are more condensed or compiled, like statistics summaries or representations of object counts and average areas.
Workflow with HSA KIT
Analysing samples and digitalisation of the slides have never been easier before. HSA KIT provides an unparallel experience to its customers, who want to keep up with todays‘ „better“ alternatives and achieve higher efficiency in their workflow. The HSA team does all that is possible to please its clients, from the installation and integration of the software to the limitless support and upgradation.
Better than alternative softwares
Strong numerical computing programmes can be used for data analysis, modelling, and simulation. Despite the fact that they have a wide range of industrial uses, there are a number of reasons why they might not be suitable for predicting scale-up in industrial processes.
- Restricted scalability: Most software are not built to handle large-scale industrial operations; rather, they are best suited for small- to medium-scale tasks. For many industrial applications, scaling up the problem size can soon become computationally expensive and time-consuming.
- Lack of industrial-specific tools: Despite all-purpose tools for data analysis, modelling, and simulation; it might not, however, have the precise models and techniques required for anticipating scale up in industrial processes, such as those associated with equipment design, safety analysis, and process optimisation.
- Restricted software integration: Industrial processes frequently require a variety of software tools and platforms, and not all work perfectly with programmes and systems already employed in the sector. The modelling and simulation process may become inefficient as a result, making it challenging to anticipate scale up precisely.
- Inadequate support for parallel computing: A single machine might not be able to handle the massive quantities of computational power needed for many industrial processes. Some parallel computing capability might not be enough to meet the needs of simulations used on an industrial scale.