Introduction to Smart Industry related to Emission Control
Smart manufacturing, also known as Industry 4.0, refers to the integration of advanced technologies and digital systems to improve manufacturing processes and enhance overall operational efficiency. One key aspect of this technology-driven transformation is the implementation of emission control and industrial air filtration systems. In this blog post, we will explore all related terminology, as well as the benefits that smart industrial manufacturing can provide.
What is Industrial Emission Control?
With Emission Control, we mean the holistic approach of handling dust, fumes, odors or any other kind of particles that are emitted within the factory, or to the outside environment. It contains solving issues related to Explosion Safety, Healthy Work Environment, Emission Limits & Compliance. It can also be used for Energy Reduction and Energy Recovery by using the heat of Industrial Scrubbers or Dust Collection Systems. By choosing for the right systems and technology, tailored to your production process, huge performance gains can be achieved.
What is the difference between digitization, digitalization, and digitalization transformation?
Before diving into how emission control and industrial air filtration systems can contribute to smart manufacturing, it is important to clarify the difference between digitization, digitalization, and digitalization transformation.
Digitization refers to the conversion of analogue information into digital formats. For example, converting paper records into digital documents.
Digitalization refers to the integration of digital technologies into various aspects of business operations. This could include the use of automated systems, digital communication tools, and other digital technologies to streamline processes and enhance efficiency.
Digital transformation, on the other hand, refers to the overall process of using digital technologies to transform an entire business model. This could involve a fundamental shift in how a business operates, including changes to products and services offered, the way customers are served, and the overall value proposition.
What are the Benefits of Smart Industrial Manufacturing?
There are several benefits of smart industrial manufacturing, including improved efficiency, increased productivity, and enhanced safety. By incorporating digital technologies into manufacturing processes, companies can better monitor production lines, reduce downtime, and identify areas for optimization. This can lead to significant cost savings and increased profitability.
In addition to these benefits, smart industrial manufacturing can also help to reduce environmental impact. By optimizing processes and reducing waste, companies can minimize their carbon footprint and reduce their impact on the environment.
While the concept of Smart Manufacturing or Industry 4.0 is known for years, the real adoption and comprehension seems to be lacking behind.
By combining the digital world and that of industrial production, “smart” manufacturing facilities are being created. Industry 4.0 solutions lead to value innovation, increased revenues, market share, and profits, mainly through much more reliable and consistent productivity and output. The empowerment to manufacture complex configure-to-order products on a mass scale, in a cost efficient way, is just one of the benefits Industry 4.0 is set out to address. Production equipment networked within such smart factories will be able to self-diagnose, and then repair, in advance of causing production down time.
Early adopters will be rewarded for their courage jumping into Industry 4.0. Those who avoid this change risk becoming irrelevant and left behind.
Smart Manufacturing Terminology
To give you a clear idea on what is what, below all relevant terminology related to Smart Manufacturing and Smart Emission Control are explained:
Smart Industry or Smart Factory
A smart factory is a factory that has the connectivity of IIoT and that has the ability to capture and store process data. In turn, a smart factory enables smart manufacturing with centralized networks that link assets and that can connect factories digitally worldwide.
Smart Manufacturing
A broad term that refers to using new technologies to improve and enable new capabilities in the physical manufacturing process. Smart manufacturing unites the world of plant operations, supply chain, product design and customer insights. Manufacturers hope to capture insights from the plant floor, through the supply chain and even from end customers.
Smart Industry or Smart Factory is a concept that refers to the integration of advanced technologies and digital systems in industrial manufacturing processes to increase efficiency, flexibility, and productivity. Smart Industry involves the use of technologies such as the Internet of Things, Artificial Intelligence, Robotics, and Big Data Analytics to optimize the entire value chain of industrial manufacturing, from design and production to logistics and after-sales service. Smart Industry aims to make manufacturing more adaptive, customer-centric, and cost-effective, while enabling mass customization and real-time optimization of production. At JOA, all our Emission Control Solutions can be adjusted or aligned according to Smart Manufacturing.
Industry 4.0
Also known as Smart Manufacturing or Industrial Internet of Things (IIoT), its objective is to help companies, especially in manufacturing and engineering sectors, and to achieve unparalleled levels of performances and efficiency.
Industry 4.0 is a term that describes the fourth industrial revolution, which is characterized by the integration of digital technologies and physical systems in manufacturing. Industry 4.0 represents a shift from traditional, linear manufacturing processes to a more interconnected and flexible approach. Industry 4.0 involves the use of advanced technologies to enable real-time monitoring, analysis, and optimization of production processes. The goal of Industry 4.0 is to create a highly connected, automated, and intelligent manufacturing environment that can quickly adapt to changing market demands and customer needs.
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(Big) Data
Data refers to any collection of information that can be analysed and interpreted to gain insights and make informed decisions. Data can be structured or unstructured and can come from various sources such as sensors, machines, software applications, social media, and user interactions. In the context of manufacturing, data can be used to monitor production processes, optimize supply chains, improve quality control, and enhance after-sales services, predict machine failures and preventive maintenance. Data is a critical component of smart manufacturing and is used to enable real-time monitoring, analysis, and optimization of production processes. By using data correctly, downtime can be reduced significantly.
Digital Transformation
Digital Transformation refers to the integration of digital technologies and processes in all areas of a business to fundamentally change the way it operates and delivers value to customers. Digital Transformation involves the use of technologies such as IoT, AI, Big Data Analytics, Cloud Computing, and Cyber-Physical Systems to digitize and automate business processes, enhance customer experience, and create new business models. Digital Transformation can help businesses to become more agile, innovative, and customer-centric, while reducing costs and increasing efficiency. In the context of manufacturing, Digital Transformation can enable Smart Industry and Industry 4.0 by integrating digital technologies and processes throughout the entire value chain of industrial manufacturing.
Digitization
Digitization is the creation of a digital representation of physical objects, analogue information or features or in other words about converting something non-digital into a digital representation. A manufacturing example would be when a measurement is converted from a manual or mechanical reading to an electronic one. For example converting paper records into digital documents.
Digitalization
Digitalization refers to enabling or improving processes by applying and integrating digital technologies and digitized data. It takes a human-driven process to software-driven process. Digitalization then brings about digitalization transformation and is the process of using digital technologies to modify or create processes, meet modern day changing business and market requirements. This could also include the use of automated systems, digital communication tools, and other digital technologies to streamline processes and enhance efficiency.
Artificial Intelligence (AI)
Artificial intelligence is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because these require human intelligence and discernment. Artificial intelligence is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. It encompasses all computer intelligence in general, from computers playing chess to autonomous vehicles.
Industrial Automation
Automation refers to the use of technology and machines to perform tasks that were previously performed by humans. In manufacturing, automation can be used to improve production efficiency and quality, reduce costs, and increase safety. Automation can involve the use of robots, machines, or computer-controlled systems to perform repetitive or dangerous tasks, such as assembly, packaging, or material handling. Read more about Automated Conveying Air Control Unit
Sensoring
Sensoring refers to the use of sensors to collect data and monitor physical and environmental conditions in manufacturing processes. Sensors can be used to measure various parameters, such as temperature, pressure, humidity, vibration, or chemical composition. The data collected from sensors can be used to optimize production processes, improve quality control, and ensure safety. Read more about Air Flow Measurement Unit
Predictive Maintenance
Predictive Maintenance is a maintenance strategy that uses data analysis and machine learning algorithms to predict when equipment or machines are likely to fail. Predictive Maintenance can help reduce maintenance costs, minimize downtime, and improve Overall Equipment Effectiveness (OEE). By analyzing sensor data, machine learning algorithms can identify patterns and predict when equipment failure is likely to occur, allowing for maintenance to be scheduled before a breakdown happens. Read more about Service & Spare Parts
(Industrial) Internet of Things
IoT or the “Internet of Things” refers to the connectivity of electronic smart devices that contain sensors which transmit and receive data through a wireless network. These smart devices are encountered throughout everyday life from mobile phones, watches, GPS tracking devices, to car and house sensors and much more. IIoT is the “Industrial Internet of Things” and is the industrial level of IoT. It consists of networked sensors and smart devices on assets and equipment in a manufacturing plant that capture an immense amount of data referred to as Big Data. The Industrial Internet of Things (IIoT) brings new possibilities for the process manufacturing industry. Originally, data was captured using hard wired sensors; today since there are new wireless sensors and devices and cloud storage capacities, a much more extensive capture and storage of process data is possible. It’s a system where physical things have IP addresses and are connected to each other through the Internet and can identify and communicate with each other.
The Industrial Internet of Things is the extension of the Internet of Things (IoT) into industrial environments. These environments require sensoring data, machine to machine communication and automation technologies together with cloud technology, machine learning and other technology.
IIoT solutions are part of the cyber-physical technologies that define the 4th Industrial Revolution, which encompasses additional solutions such as additive manufacturing, digitizing business processes, and advanced control systems. For manufacturers this translates to improved sustainability, less downtime, and more profitability across the factory floor.
In manufacturing, the IoT can enable real-time monitoring of equipment and production processes, predictive maintenance, make remote control possible and optimization of operations. The IoT can also improve supply chain management, reduce energy consumption, and enhance product quality.
Process Improvements
Process Improvements refer to the implementation of changes to manufacturing processes to increase efficiency, reduce waste, and improve product quality. Process improvements can involve the use of new technologies, changes to equipment, or improvements in workflows and procedures. By continuously improving processes, manufacturers can reduce costs, increase productivity, and meet changing customer demands.
Trend Analysis
Trend Analysis is the process of identifying patterns and trends in data over time to gain insights and inform decision-making. In manufacturing, trend analysis can be used to monitor production processes, identify areas for improvement, and optimize resource allocation. By analyzing data trends, manufacturers can identify potential issues before they become major problems, enabling proactive decision-making and improved process control.
Machine Learning
Machine learning the use of algorithms to find patterns in data and automatically learn from this to make decisions, predictions or determinations about the future. The objective is to allow computers to learn without human intervention and to modify actions accordingly. Machines train themselves without external coding needed. It recognizes patterns and allows a system to make predictions based on these patterns and data it receives.
Machine Learning is a form of artificial intelligence that involves the use of algorithms and statistical models to analyse data and make predictions or decisions. In manufacturing, machine learning can be used for predictive maintenance, quality control, supply chain optimization, and other applications. By analysing data patterns and learning from experience, machine learning algorithms can improve accuracy, speed, and decision-making in manufacturing operations.
Cybersecurity
Cybersecurity in an industrial manufacturing and Industry 4.0 context refers to the protection of critical information, systems, and assets from cyber threats and attacks in the context of the fourth industrial revolution, known as Industry 4.0. Industry 4.0 is characterized by the integration of digital technologies into manufacturing processes, enabling smart factories and highly connected systems. While these advancements bring numerous benefits, they also introduce new cybersecurity challenges that need to be addressed to ensure the safety and reliability of industrial operations.Terms related to cyber security are:
- Data Protection: Protection of sensitive information, like through encryption, access controls, ‘one-way communication’
- Network Security: With interconnected devices and systems. Becoming more complex and susceptible to cyber threats. Examples are firewalls, intrusion detection, secure communication protocols and prevention systems. Helps protect against unauthorized access and data breaches.
- Cloud Security: Often organized by Cloud Service providers. It provides data storage, processing and analytics. Prevents data leaks, unauthorized access and service disruptions.
- Employee awareness and training: Raises awareness among employees as they are a significant factor in potential incidents and helps prevent events.
- Incident response and Recovery: Making sure that the right actions are taken during an event.
Cloud Computing
Cloud computing is a transformative technology that has revolutionized how businesses and industries operate, including smart industrial manufacturing. It refers to the delivery of computing services over the internet, allowing users to access and utilize a vast array of resources, such as storage, processing power, and applications, without the need for on-site infrastructure. In the context of smart industrial manufacturing, cloud computing enables manufacturers to store and analyze large volumes of data generated by sensors, machines, and processes in real-time. This data can be processed using sophisticated algorithms and artificial intelligence, facilitating predictive maintenance, optimizing production processes, and enhancing overall efficiency. Additionally, cloud-based solutions foster collaboration, as relevant stakeholders can access and work on the same data from anywhere, leading to agile decision-making and streamlined operations.
Network Effects
Network effects play a crucial role in the advancement of smart industrial manufacturing. The concept refers to the idea that the value of a product or service increases as more people or devices use it. In this context, as more industrial devices and machines become interconnected within a factory or across a supply chain, the efficiency and effectiveness of the entire system improve significantly. Each additional connected device contributes to the overall intelligence and data pool, allowing for better insights and optimization. As more manufacturers adopt smart industrial solutions, the benefits multiply, encouraging further adoption and creating a positive feedback loop. The network effects not only improve the performance of individual factories but also enable global optimization across multiple facilities, fostering an interconnected and optimized industrial ecosystem.
Digital Twins
Digital twins are virtual replicas of physical assets, processes, or systems. They form a pivotal component of smart industrial manufacturing. By integrating real-time data from sensors and other sources, digital twins simulate the behaviour and performance of physical entities in a virtual environment. In the context of smart industrial manufacturing, digital twins are employed to monitor and analyse the operation of machinery and production processes. This technology allows manufacturers to gain a comprehensive understanding of their equipment’s health, predict potential issues, and optimize performance.
By running simulations and what-if scenarios, manufacturers can also test different strategies and adjustments without impacting the physical production process. Digital twins facilitate predictive maintenance, minimize downtime, and enhance overall productivity, leading to cost savings and increased competitiveness in the industry.
Additive manufacturing
refers to 3D printing and 3D printed parts being used in the manufacturing process. The technology behind additive manufacturing is still being developed but will certainly be utilized by many companies in the future.
Interoperability
Allows machines, sensors, actuators, computers, robots, and humans to freely and easily pass information to each other in the smart factories that have implemented Industry 4.0 principles.
