Last updated on 2024年5月9日

What are some emerging trends and technologies in process modeling and control?

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Process modeling and control are essential skills for designing, optimizing, and managing complex industrial, biological, and environmental systems. In this article, you will learn about some of the emerging trends and technologies that are transforming the field of process modeling and control, and how they can help you improve your performance, efficiency, and sustainability.

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Process mining:

This tool provides a clear picture of current workflows, highlighting inefficiencies and quantifying their impacts. It allows for prioritizing improvements and can trigger automatic corrections, ensuring that process execution drives continuous design refinement.
Embrace AI and ML:

Artificial Intelligence (AI) and Machine Learning (ML) predict and automate tasks, boosting process efficiency. By learning patterns and making data-driven decisions, they help you stay ahead of the curve in dynamic business environments.

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Raghav R.

Ex Co-Founder | A Millenial by Birth &…
Jan Ploeg

Data & Analytics ? Process Intelligence…

1 Digital twins

A digital twin is a virtual replica of a physical system that can be used to simulate, monitor, and optimize its behavior and performance. By using data from sensors, actuators, and other sources, a digital twin can create a dynamic and accurate representation of the system's state, inputs, outputs, and interactions. Digital twins can help you reduce costs, risks, and errors, enhance quality and reliability, and enable faster and smarter decision-making.

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Raghav R.

Ex Co-Founder | A Millenial by Birth & Gen Z by Profession | Storyteller | Author- "Startup your career"- Amazon #2k rank | 2nd book in progress | Sattwic Guna..
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"Sticking to old process models is like using a flip phone in the era of smartphones." Here's how to upgrade: 1. AI & ML: Predict and automate processes. 2. Low-Code Platforms: Enable easy process customization. 3. Process Mining: Identify inefficiencies through data. 4. Digital Twins: Simulate processes for optimization. 5. Blockchain: Secure and transparent workflows. 6. IoT: Monitor processes in real-time. 7. Cloud Solutions: Scale and collaborate anywhere. Embrace these trends to future-proof your processes.

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2 Artificial intelligence

Artificial intelligence (AI) is the ability of machines to perform tasks that normally require human intelligence, such as learning, reasoning, and problem-solving. AI can enhance the capabilities of process modeling and control by providing advanced methods for data analysis, pattern recognition, optimization, and adaptation. AI can help you discover hidden insights, generate novel solutions, and cope with uncertainty and complexity.

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3 Cyber-physical systems

A cyber-physical system (CPS) is a system that integrates physical components with computational processes and communication networks. A CPS can enable real-time feedback, coordination, and control between the physical and digital domains, creating a seamless and interactive loop. CPS can help you improve safety, security, and resilience, as well as enable new functionalities and applications.

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4 Cloud computing

Cloud computing is the delivery of computing services, such as servers, storage, software, and analytics, over the internet. Cloud computing can offer you scalability, flexibility, and accessibility for your process modeling and control needs. You can access and store large amounts of data, run complex simulations and models, and collaborate and share information with others, without investing in expensive and dedicated hardware and software.

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5 Internet of things

The internet of things (IoT) is the network of physical objects that are embedded with sensors, actuators, and other devices that can communicate and exchange data with each other and with other systems. IoT can provide you with rich and diverse data sources, as well as new opportunities for automation and integration. IoT can help you monitor and control your processes remotely, efficiently, and intelligently.

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6 Model predictive control

Model predictive control (MPC) is a control technique that uses a mathematical model of the system to predict its future behavior and optimize its inputs accordingly. MPC can handle multiple inputs and outputs, constraints, disturbances, and uncertainties, and can adapt to changing conditions and objectives. MPC can help you achieve optimal performance, robustness, and stability for your processes.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Jan Ploeg

Data & Analytics ? Process Intelligence ?? Intelligent Automation ? Agile Delivery ?
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Process Mining provides a fast and accurate factual basis for your process modelling efforts. It shows how a process runs at the moment and not only informs you of the issues in that process, but also quantifies the impact. This allows you to prioritize and scope your process improvement initiatives. In addition, process mining technoloqy can be used to continuously monitor conformance of your process execution with your process blueprint, alert (looming) deviations and trigger automated corrections. And the other way around: it can lead to the conclusion that elements of your process design need updating, because they don't reflect reality anymore. In this way, process execution itself drives continuous improvement of the process design.

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Ahmed Ragab

Process manager (LABIX & TPX ThaiOil affiliates) | Freelance Business Consultant for polymer industries | Passionate about AI and Polymer science| Helping Polymer business raise profitability| Chartered Engineer (CEng)
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Hybrid modeling using the conventional modeling and basic principles /equation of state and fine tune with data analysis to get better model mimic the real equipment, this enhance not only the reliability and optimization but also improve plant process safety.

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What are some emerging trends and technologies in process modeling and control?

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1 Digital twins

2 Artificial intelligence

3 Cyber-physical systems

4 Cloud computing

5 Internet of things

6 Model predictive control

7 Here’s what else to consider

Process Modeling

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