SIRIM STANDARD · 2020. 7. 28. · real-time data on production status and performance of the...

SIRIM STANDARD

SIRIM <=XXXX=>:2020

ICS: 35.240; 35.020;25.040

Industry 4.0 - Industry application/use case for food and beverages (F&B) and

chemical industries

© Copyright 2020

SIRIM Berhad

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SIRIM STANDARD SIRIM Berhad is a premier total solutions provider in quality and technology innovations that helps industries and businesses to compete better through every step of the business value chain. SIRIM Berhad is the centre of excellence in standardisation, facilitating industries and businesses in enhancing their production and competitiveness, protecting consumers’ health and safety, and giving them the choice for quality products and services. As a standards development organisation, SIRIM Berhad has extensive expertise in standards research and consultancy which helps industries and businesses to meet local and international requirements and practices, through the development of SIRIM Standards. SIRIM Standards are developed according to SIRIM standardisation procedures, which are in line with international practices that ensure appropriate notification of work programmes and participation of interested parties. SIRIM Standards are developed through consensus by committees, which consist of experts in the subject matter. The use of SIRIM Standards is voluntary, and it is open for adoption by regulators, government agencies, associations, industries, professional bodies, etc.

For further information on SIRIM Standards, please contact: Standards Research and Development Department SIRIM STS Sdn Bhd (Company No. 448249 - A) 1, Persiaran Dato’ Menteri Section 2, P.O. Box 7035 40700 Shah Alam Selangor Darul Ehsan MALAYSIA Tel: 60 3 5544 6314/6909 Fax: 60 3 5510 8830 Email: [email protected] http://www.sirimsts.my

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Contents

Page Foreword ............................................................................................................................... ii 0 Introduction ............................................................................................................... 1 1 Scope ........................................................................................................................ 1 2 Normative references ................................................................................................ 2 3 Terms and definitions ................................................................................................ 2 4 Abbreviated terms ..................................................................................................... 3 5 Summary of use case scenarios ................................................................................ 3 6 Use case scenarios ................................................................................................... 6 Annex A Industry 4.0 fundamental technologies ............................................................... 27 Bibliography ........................................................................................................................ 33

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ii © SIRIM Berhad 2020 - All rights reserved

Foreword This SIRIM Standard was developed by the Project Committee on Industry 4.0 - Industry Application/Use Case established by SIRIM Berhad. This standard was developed with the following objectives: a) to provide examples to demonstrate that innovative processess for linking plant

equipment, IT systems and business model more closely are already being developed and implemented by companies and research institutions;

b) to assist manufacturing organisations to select and use the examples within their own manufacturing environment; and

c) to provide guidelines for organisations to implement and progress towards becoming more efficient market-responsive value chain and customer-focused Industry 4.0 organisations.

This standard will be subjected to review to reflect current needs and conditions. Users and other interested parties may submit comments on the contents of this standard for consideration into future versions. Compliance with this standard does not by itself grant immunity from legal obligations.

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© SIRIM Berhad 2020 - All rights reserved 1

Industry 4.0 - Industry application/use case for food and beverages (F&B) and chemical industries

0. Introduction Industries are facing many challenges as digital revolution will affect manufacturing in a similar way as it is happening to communication, consumer markets and services. Technological advances have enormous potential to make the society more efficient and digitally inclusive and industry 4.0 implementations are demonstrating convergence of information and communications technology and their widespread application. Fundamental technologies for industry 4.0 which are explained in Annex A include: a) internet of things (IoT); b) big data analytics; c) cybersecurity; d) cloud computing; e) additive manufacturing; f) artificial intelligence; g) advanced materials; h) simulation; i) augmented reality; j) autonomos robots; and k) system integration. Understanding the application of industry 4.0 made it easier to identify categories and highlight use case commonalities. All selected use cases have real-world validity. Gaps were filled by adding extra use cases and future developments were also considered.

1. Scope This standard provides fourth industrial revolution application and use cases based on real-world applications and requirements. This standard comprises eleven use cases for industry 4.0 technologies by companies and research institutions specifically in Malaysia for food and beverages and chemical industries. Use cases are a well-known tool for expressing requirements at a high level and demonstrating their real-life relevance. The use cases provide a practical context for considerations on interoperability and standards based on user experience. The use case scenarios are intended to illustrate typical industry 4.0 use cases but are not meant to be an exhaustive list of realisations within an industry 4.0 environment. This standard can be used to assist in the identification of potential areas for standardisation in the industry 4.0 environment to ensure ease of operation and interoperability.

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2. Normative references There are no normative references in this standard.

3. Terms and definitions For the purposes of this standard, the following terms and definitions apply. 3.1 actor Entity that communicates and interacts. [SOURCE: PD ISO/IEC TR 22417:2017]

3.2 automation Conversion of processes or equipment to automatic operation, or the results of the conversion, to monitor, control and execute tasks. 3.3 connectivity Interconnection of Information Technology (IT) and Operational Technology (OT) to enable communication and seamless data exchange. 3.4 integration System condition or activity to realise the condition in which components of a system are organised to collaborate, coordinate and interoperate while exchanging items, as needed, to perform a system’s task. 3.5 intelligence Acquisition, processing and analysis of data by machine and equipment to make decisions in line with Cyber-Physical System (CPS) levels. 3.6 IoT use case Description of a hypothetically possible situation where IoT concepts, products and services may be specified as a set of actions associated with actors in an IoT system, which yields an observable result that is, typically, of value for one or more actors or other stakeholders of the system. NOTE. The aim is to pictorially describe a field of problems in a way that the artificial situation makes IoT approaches to solutions evident in their temporal, spatial as well as technical dimension.

[SOURCE: PD ISO/IEC TR 22417:2017]

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3.7 real-time Communicated, shown, presented, etc. at the same time as events actually happen. [https://dictionary.cambridge.org/dictionary/english/real-time]

3.8 use case Specification of a sequence of actions, including variants, that a system (or other entity) can perform and interact with actors of the system. [SOURCE: ISO 14813-5:2010, B.1.160]

4. Abbreviated terms IoT Internet of things

AI Artificial intelligence

F&B Food and beverages

FIFO First-in-first-out

FMS Fleet management system

OEE Overall equipment effectiveness

HDPE High density polyethylene

LDPE Low density polyethylene

UV Ultraviolet

RO Reverse osmosis

HACCP Hazard analysis and critical control points

CLO2 Chlorine Dioxide

TDS Total dissolved solids

5. Summary of use case scenarios The use case scenarios are intended to illustrate typical industry 4.0 use cases. The summary of the use case scenarios with a short description of the use cases are as in Table 1.

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Table 1. Summary of use case scenarios

Use case

number

Name of use case

Description Type of technology

(Industry 4.0)

Context of use

1 Water treatment plant health monitoring

This example applies retrofitting of water treatment plant with sensors and monitoring equipment.

Cloud computing System integration IoT

F&B Chemical

2 Automated trolley flow management system using auto guided vehicle

Automated guided vehicle is a mobile robot that can be used to transport many different types of materials including pallets, rolls, racks, carts, trolleys and containers. Its application is to move materials around a manufacturing facility or a warehouse.

Autonomous robots

F&B

3 Temperature and humidity sensor (Rival sensor)

This example applies IoT and cloud computing to collect temperature, humidity and sound data concurrently. The sensor provides real-time data without human intervention.

IoT Cloud computing

F&B Chemical

4 Smart weighing (Varos weighing)

This example applies cloud computing and QR code tracking. It is a smart weighing platform, collecting data instantly and store data in cloud. Based on QR code tracking, it is able to access the information instantly.

Cloud computing F&B Chemical

5 Artificial Intelligence (AI) sorting machine

This use case presents an AI solution for identification and selection of raw materials (chilies) with specified qualification (same colour and quality).

AI F&B

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Use case

number

Name of use case

Description Type of technology

(Industry 4.0)

Context of use

6 3D food printing

The design of the food is created by computer-aided design (CAD) software and the 3D printer is connected via USB to the computer. The food deposition was fully controlled digitally by the computer. 3D food printing involves 3D model building, object printing and post-treatment.

Additive manufacturing

F&B

7 Chemical product tracking system

This example describes the process of product tracking system using IoT to capture real-time data on production status and performance of the chemical products.

IoT Chemical

8 Predictive maintenance

This example applies big data analytics and machine learning application for a predictive capability (predictive maintenance), so engineers working on data analysis can deal with uncertainties in the preventive measures.

Big data analytics AI

Chemical

9 Optimisation of ethane supply chain

This example applies advanced process control with machine learning application to optimise the production of ethane while automatically controls the plant distributed control system.

Big data analytics AI

Chemical

10 Optimisation of energy usage

This example describes an AI, IoT and big data application being used to monitor chemical processing plant to enable optimisation of energy usage.

Big data analytics AI IoT

Chemical

11 Health, Safety and Environmnet (HSE) for transport safety

This example describes an IoT, big data analytics and machine learning application being used to track the driving behaviour and pattern of the drivers.

IoT Big data analytics

Chemical

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6. Use case scenarios 6.1 Water treatment plant health monitoring (use case number 1 in Table 1) 6.1.1 General This use case describes an IoT application for comprehensive management of a water treatment plant health monitoring in F&B industry. In the water treatment plant, various types of sensors, which are in collaborating networks, perceive the information of device status and running environment of the water treatment plant and transmit this data to the IoT gateway which collects and formats the data. The application software can analyse, process and fuse the data to provide convenient and reliable services for the managers and operators. With the IoT application for this water plant, it contributes to the technical support for secure, reliable, highly effective operation and promotes the technical progress of the industry. 6.1.2 Narrative of use case Traditional management and operations of the water treatment plant is largely based on labourers’ field inspection by seeing, hearing, touching, smelling and oversighting of the machine and ambient environment in the plants. However, with low efficiency methods and a lack of qualified workers the problem cannot be solved without using technology. Nowadays, many new devices and technologies are applied in water treatment plants and therefore smarter and leaner management capabilities need to be evolved. Traditionally there are a water treatment plant system which will process the raw water to use in the production of beverages. This treatment system includes process of chlorine, sand filter, carbon filter, water softener, UV and RO processing systems. All the data and analysis have been done manually by testing in lab. It reduces the possibility of data inaccuracy and human error due to manual analyses in testing lab. A retrofitting for the existing water treatment plant is done by integrating the sensor and monitoring equipment. The equipment will monitor the data of the main parameter such as pH, chlorine, chlorine dioxide, hardness, TDS of the water and UV system. All data will be connected to the cloud system for storage, monitor in web portal and alarm system. The retrofitting of the water treatment plant resulted in automated water quality and condition monitoring, real-time data collection and monitoring of critical parameter; and immediate inline corrective actions during the production. These automated data captured for pH, CLO2, TDS and hardness of treatment water system can be analysed and recorded accurately. This system also reduces time consumption during testing and analysis of machine health data in the lab. All data stored from manual system to automated system in a cloud-based system can easily be visualised for monitoring purposes either through a personal computer (PC) or smartphone. 6.1.3 Conclusion Through the use of retrofitting, comprehensive and smart monitoring and controlling systems for the water treatment plant can be developed. In the front end, sensor networks sense the environment parameters which impact the ordinary running and real-time status of devices.

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All this information is transmitted to the service platform to be processed and analysed, and can be used to produce the visualisation interface which displays the data and central controlling system. Smart monitoring and assistive controlling systems in water treatment plant can greatly reduce the workload and improve the quality of operation and management. This can prevent or reduce the failures caused by missing safeguards, over-worked staff, weak asset management, defective devices, inappropriate operation and environment parameter exceeding normal setting range; thereby greatly improving work efficiency and reduce operation and maintenance costs. Figure 1 shows the system architecture of water treatment plant health monitoring.

Figure 1. System architecture of water treatment plant health monitoring

6.2 Automated trolley flow management system using an auto guided vehicle (AGV) (use case number 2 in Table 1) 6.2.1 General This use case is an application of an auto guided vehicle (AGV) in the F&B industry. AGV, an automated guided vehicle is a mobile robot that can move materials around in a manufacturing facility or a warehouse. The AGVs are integrated with the fleet management system to dynamically transfer trolleys with semi-finished products in the production line from one station to another. The main factor that contributes to the use of AGV is because transferring/handling semi-finished products manually is time consuming. The second significant factor is high labour cost and inconsistent handling of semi-finished materials.

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6.2.2 Narrative of use case Labour intensive of handling semi-finished products, inconsistent handling of trolleys due to first-in-first-out (FIFO) issue and ineffective production flow management system in the factory are the factors for the application of the AGVs and the Fleet Management System (FMS). AGVs are widely used for their advantage which is the ability to move from one place to another without supervision by human or operators. Operating on battery power and with a web-based operation system using FMS, AGV is able to perform all instructions accurately. A map can be created in the FMS based on the route saved in the system to run the AGV. The management of the flow of data and information allows both real-time monitoring of the shop-floor activities and rapid decision making. These have improved the productivity of the facility. With the AGV and the FMS, production is able to run without compromising the labour productivity which may be hampered as a result of distractions due to managing and transferring semi-finished products manually from one production area to another. AGV is able to eliminate rejection rates due to mishandling of trolleys by improving the FIFO issue. Seamless production flow/transfer ensures no deadlock in trolley flow management and disruption in production since the trolleys are intelligently managed by the AGV. These new retrofitting system and layout have saved costs and has maximised the Overall Equipment Efficiency (OEE) in the factory. 6.2.3 Conclusion The system aims to reduce operating costs (man hour) by freeing up the operators who previously moved the trolleys for other duties. Every task is optimised in order to eliminate rejection and perform requests in the shortest possible time; this means an increase in productivity, maximise OEE and improve the product quality. The overall outcome of this use case able to reduce 50 % of labour costs, improved production flow and eliminate material waste. Figure 2 shows the application of AGV in handling the trolleys.

Figure 2. Trolleys handled by an AGV

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6.3 Temperature, humidity and sound sensor (Rival sensor) (use case number 3 in Table 1) 6.3.1 General Food industries are required to keep track of temperatures and humidity to maintain food safety and human safety standards at plant. These data are important to make product quality related decisions, as they affect the product quality directly or indirectly. Also, factory environment impacts bacterial growth count of finished goods. Collecting these data manually uses a lot of time, and more time is wasted to further tabulate and analyse the data. 6.3.2 Narrative of use case Temperature, humidity and sound are vital information in HACCP food safety standards. Daily records for temperature and humidity need to be maintained. Collecting and analysing data based on these preferred parameters can be very difficult and time consuming. With manual reporting, analysis cannot be performed daily. Periodic analysis cannot help to make timely decisions. Manual analysis requires extra time and extended man hours. Temperature, humidity and sound sensor (Rival sensor) is able to collect temperature, humidity and sound data concurrently. With the help of truly wireless technology, battery powered and wi-fi connectivity, it allows the company to place the sensor wherever they need to log the data, provided with wi-fi coverage and able to send the data to the cloud. The data collection frequency starts from the first second. The sensor has been manufactured for continuous data collection, storage and data analysis. Data can be downloaded by one click of download button via Pandora software. The data can be compared using line chart analysis. The sensor (Rival sensor) provides real-time data without human intervention. Figure 3 shows cloud display screen for temperature, humidity and sound sensor. 6.3.3 Conclusion It has been proven that IoT sensors will automatically help collect, process, analyse and store data in cloud. With automated data collection, it will be easy to analyse data with just one click. The sensor will reduce man working hours, so the company can allocate more time for other jobs or tasks.

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Figure 3. Cloud display screen for temperature, humidity and sound sensor

6.4 Smart weighing (Varos weighing) (use case number 4 in Table 1) 6.4.1 General Weighing is probably the most important process during manufacturing. Weighing scales are widely used equipment for SMEs to calculate material formulation or final output. Varos weighing is a smart weighing platform, collecting data instantly and storing them in cloud. Based on Quick Response (QR) code tracking, it is able to access the information instantly. 6.4.2 Narrative of use case Traditional platform weighing scales consume more manpower to arrange materials for weighing. Data collection is done manually and is prone to errors. Weighing is an important process in product routing. Wrong data will lead to wrong formulation and loss of materials; hence the data will be rejected and waste human efforts and money. Due to wrong calculations, products will be rejected and it will affect OEE. Improvement made by using Varos is that data collection can be harmonised (in-line) with minimum effort. It is a portable weighing solution which workers do not need to push the heavy materials the to weighing station as it can move towards heavy materials, reducing human manpower. With less manpower and automated real-time data collection, performance and efficiency will be improved. Figure 4 shows smart weighing scale server display. FOR S

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Figure 4. Smart weighing scale server display

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6.4.3 Conclusion Varos will be one of the best solutions for SMEs as they need to update inline weighing scale data manually. Varos will help SMEs by automated data collection together with product routing. Varos will be effective with one manpower who is able to do weighing, data collection and analysis just one click away. 6.5 Artificial Intelligency (AI) sorting machine (use case number 5 in Table 1) 6.5.1 General Challenges in food quality include consistency in colour, taste and aroma. All these can be solved with selection of the raw materials within the specifications. The selection of raw materials is a very important process in food manufacturing. This process is mainly handled by humans manually with some test methods. The results can be different due to operator bias. 6.5.2 Narrative of use case Processing chili (or other natural spices) powder with the same colour and quality on every batch is difficult. Being naturally grown, there are discrepancies between each chili and raw materials shall undergo sorting process. Quality, colour and aroma will be evaluated differently between human beings. Hence, quality controller (QC) will have different selection processes even with the same standard operating procedures (SOP) in place. It is tedious to achieve the same quality consistency for every batch. The use of AI sorting machine will increase the sorting speed with higher accuracy. Rejection rates can be known instantly which can help to improve OEE. AI sorting machine can be handled by any unskilled employee without much supervision or prior training. Data collection including rejection, wastage and other details can be calculated automatically. Figure 5 shows AI for chili display. 6.5.2 Conclusion AI sorting machine can be handled by any employee without any prior training/experience or supervisions. Accuracy on selection process will be more consistent compared to human selection. Also, it reduces manpower and improves efficiency and OEE.

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Figure 5a. AI chili display (attention needed)

Figure 5b. AI chili display (reject)

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Figure 5c. AI chili display (good) 6.6 3D food printing (use case number 6 in Table 1) 6.6.1 General Additive manufacturing is a technical term describing technologies of building complex 3D geometrics from 3D computer images by adding layer-by-layer material until the final desired 3D images are constructed. The material can be plastic, metal, concrete or special material such as polymer or food. 3D printing includes technologies like selective laser sintering (SLS), fused deposition modelling (FDM), laser-assisted bioprinting, micro-extrusion, etc. 3D food printing is food layer manufacturing using fused deposition modelling (FDM) method.

3D food printing is appreciated for its use in the manufacturing of food based on new product formulation and also beneficial to be used for mass customisation of food that is fully personalised, based on customer-driven data dietary values. The customisation in the 3D food printing can either be due to health issues (sensitive foods, digestive problem, difficulty in swallowing and mastication, etc.) or consumer preferences (low calorie, reduced sugar, low salt, etc.). 6.6.2 Narrative of use case 3D food printing is developed for the fabrication of food for personalised diet, for this use case, the elderly group. One of the common eating disorders associated with the elderly is dysphagia. Dysphagia is a disorder in which one is unable to swallow or transfer food or liquids from the mouth to the stomach. The condition may lead to malnutrition and loss of the quality of life among the elderly. The elderly with dysphagia problem needs texture modified food; the food with properties that are adapted to their eating capabilities, with increased in appearance and enriched nutrition. These diet modifications complement with visually appealing 3D printed food will provide an aesthetic aspect that is valued and motivates their nutritious food intake.

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3D food printing incorporates 3D printing technology and digital gastronomy. The design of the food is created by a computer-aided design (CAD) software and the 3D printer is connected via USB to a computer. The food deposition is fully controlled digitally by the computer. A defined amount of food is squeezed throughout the nozzle, adding layer by layer until the desired design is achieved. The 3D food printing operation is usually limited to dosing, mixing, squeezing and aggregation of the specific material with specific properties. The food ink ingredients should have a standard flow capability, with a range of viscoelasticity and particle sizes to meet the rheological and post-deposition adhesion characteristics. 3D food printing involves 3D model building, object printing and post-treatment. The process begins with the idea of the 3D printed object of the food, and then the design is converted to 3D modelling, then later, the G-code interpretation and generation of the design. The G-code is then used for 3D food printing using defined food materials. The printed food may undergo minimal post-processing such as baking or cooking for stabilisation of the structures. 3D food printers which can be classified into industrial and domestic scales are generally supplied with web service or digital platform to allow consumers to import food designs, download recipes and control the printing process. Figure 6a gives an overview of 3D printing process. Personalised nutrition for seniors and patients in hospitals, personalised homemade meals, and 3D food printing vending machine are examples of 3D food printing future potentials which offer consumers personalised meals to support their individual needs associated to health and well-being. Incorporation of IoT, artificial intelligence and machine learning would allow personalised 3D printed food via a smart device.

Figure 6a. The overview of 3D printing process (Sources: Sun J., Zhou W., Huang D., Yan L.

(2018) 3D Food Printing: Perspectives. In Gutiérrez T. (eds) Polymers for Food Applications. Springer, Cham)

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Biozoon, a company based in Germany has successfully printed ‘chicken drumstick’ from mixtures of broccoli, cauliflower and potato puree using 3D food printing for the elderly. Singapore Institute of Technology (SIT) and Massey University, New Zealand research team collaboration managed to produce a 3D printed reduced-calorie durian and fortified them with minerals for elderly food. On the other hand, a group of Massachusetts Institute of Technology (MIT), USA researchers used calorie intake feedback and hungriness level from users to control the infill pattern and infill density of 3D printed cookies to achieve a different degree of satiety for the users. Figure 6b shows the workflow of FoodFab, a system that is designed to control consumers’ calorie intake by personalising food textures using 3D food printing by MIT. Presumably, we can manipulate a dish of ‘nasi lemak’ for the elderly. For example, ‘nasi lemak’, an adjustment on the calories intake (480 kilocalories) by the portion of rice, sambal, boiled egg, cucumber and anchovies. The rice, sambal, boiled egg, cucumber and anchovies are turned into puree-like textures to satisfy the elderly's feeding capabilities. In order to make them suitable for extrusion used in 3D printing, hydrocolloids are added to these. Xanthan gum and pectin are examples of commonly used hydrocolloids. The most important factor in food is mouth feels and its perceptible texture. Traditional foods like nasi lemak after grinding and modifying by additives can be successfully 3D printed and subjected to various processing steps. The viscosity, consistency and solidifying are the properties that determine the printability of food components. Figure 6c shows 3D by byFlow, one of the examples of 3D food printers that is available in the market. 6.6.3 Conclusion 3D food printing can be a platform for achieving industry 4.0 for small and medium-sized companies in evolving new and emerging technologies. However, since the development of 3D food printing is still primitive, it appears to be feasible for specialised food applications with a heavy focus on custom product design or manufacturing. There is also a limitation on the selection of the food materials for the food ink as not every food is suitable for print. In general, 3D food printing denotes a breakthrough in the custom food supply chain for a niche market.

(Source: it.edu/research/foodfab/foodfab.html)

Figure 6b. Workflow of FoodFab using 3D food printing by Massachusetts Institute of

Technology, USA

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(Source: it.edu/research/foodfab/foodfab.html)

Figure 6c. 3D by byFlow, an example of 3D food printer 6.7 Chemical product tracking system (use case number 7 in Table 1) 6.7.1 General The product tracking system is to capture real-time data on production status and performance of chemical products using the IoT technology. A normal process flow for bottled liquid packaging includes chemical formulation, bottle filling and capping, labelling and packing. The system uses sensors and IoT devices that are installed at each process to count the product quantity and send the data to the cloud server for processing. A dashboard system is developed to display and monitor the product quantity and their production status. 6.7.2 Narrative of use case An automated production line consists of machines that are capable of loading the bottles, filling the chemical liquid in four bottles at one time, hard pressing the stopper and turning the cap on each bottle. Usually, during the production setup, one or two bottles will be stuck at each process due to machine speed, conveyor speed, chemical spillage, inadequate filling, etc. Therefore, it is difficult to indicate in real-time which process that contributes more to the delay in one production cycle. Consequently, the machine timing for performing each process is adjusted based on trial and error basis. This is time consuming and results in the machine availability loss. Furthermore, data and information related to the production such as product quantity, OEE are taken manually and recorded in the physical files, hence they are susceptible to human error and data losses. Report on actual production is done after all the job is completed, subsequently will cause a delay in performing the business forecast and predicting next sales and production. The IoT devices and IR sensors are retrofit to the conveyor of each process of the production line. During the setup, the time of each bottle that passes the sensors is captured automatically, sent to the cloud server and displayed on the dashboard. With the time reference, the machines and conveyor are adjusted accordingly and properly synchronised so that smooth filling and capping process can be achieved.

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Every transaction of the bottles right from the loading up to the finished capping is stamped. The top management can immediately know the quantity of the day’s production and estimate for the next. Since the data is digitally recorded, the reporting is much easier and proper arrangement for the next raw materials purchasing can be done. The time stamped for each process expedites the calculation of the machine performance rate, thus percentage of OEE can be known and total productivity is improved. With the dashboard, the product quantity and production status of chemical products can easily be visualised for monitoring purposes either through a PC or smartphone. Figure 7 shows the dashboard for chemical product tracking. 6.7.3 Conclusion Chemical industries should use innovative technology to perform digital transformation by employing IoT technologies in order to work more efficiently and produce better results. The usage of sensors can be expanded to measure temperature, pH, humidity, etc. which are important parameters to maintain chemical solutions. The monitoring of chemical solutions and products can be done remotely with the IoT technology thus provides fast decision making for the next sales and production.

Figure 7. Dashboard for chemical product tracking

6.8 Predictive maintenance (use case number 8 in Table 1) 6.8.1 General In the past, most company XY plants have adapted Advanced Process Control (APC) system, which provided the plant with some process-related prediction capability to anticipate the changes that happened in the plant process and the company took proactive steps to combat those changes by means of adjusting the process parameters; however, this was unfortunately limited to the process control. This use case applies big data analytics and machine learning application in improving and automating certain aspects of day-to-day operations.

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6.8.2 Narrative of use case Company XY has many ageing assets and equipment, and each equipment has to be in good shape in order for production (e.g. crude oil, gas, chemicals, petroleum products, etc.) to remain continuous to meet customer demand. Even the newer equipment needs to be taken care of to ensure that they are able to perform as designed through its expected lifetime. One of the ways company XY manages this is by conducting predictive maintenance of each critical equipment manually. This however is very labour intensive and time consuming, with so many equipment to be monitored. Previously, the maintenance team will go around to measure the critical parameters of each equipment, record the data and monitor it manually. They will prescribe certain improvement recommendations for degrading equipment from their monitoring from time to time. Due to the manual nature of this activities, some equipment were “off the radar”, and sometimes the recommendation came too late whereby the equipment has failed prematurely. Installation of sensors on each critical equipment which measures the main parameters of the equipment, coupled with other sensors around the process area, provides the data analytics models which will give real-time assessment on each of the critical equipment that is being monitored. This enhances the detection of anamolies, prediction of mean-time-between-failures and performance deterioration accuracy, automatically triggers the preventive measures required for each equipment, reduces manpower labour requirement and increases productivity of the plant in general. Figure 8 shows schematic diagramme for predictive maintenance programme flow. 6.8.3 Conclusion The implementation of predictive maintenance programme with assistance of data analytics and machine learning models increases prediction of equipment failure accuracy which ensures that the correct preventive measures are taken before failure thus reduces unwanted plant shutdown due to equipment failure.

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Figure 8. Predictive maintenance programme flow

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6.9 Optimisation of ethane supply chain (use case number 9 in Table 1) 6.9.1 General In the past, most company XY plants have adapted Advanced Process Control (APC) system, which provided the plant with some process-related prediction capability to anticipate the changes that happened in the plant process. The company took proactive steps to combat those changes by means of adjusting the process parameters; however, this was unfortunately limited to the process control. This use case applies big data analytics and machine learning application in improving and automating certain aspects of day-to-day operations. 6.9.2 Narrative of use case Ethane is the main component in the production of polyethylene which is used to make recyclable HDPE and LDPE plastics products. The attractive price of polyethylene drives our ethylene plant to optimise plant processing conditions in order to maximise ethane production. However, more often than not, this has to be done manually by plant operators, and due to the manual nature of the control action taken, there is a “leakage” of ethane along the ethylene and polyethylene plants (i.e. ethane production is not maximised due to the process conditions and lack of parameter control). Application of Advance Process Control (APC), coupled with machine learning models in the plant control system provides prediction and optimum process parameters in order for the plant to optimise ethane production, and controls the plant Distributed Control System (DCS) automatically to reduce human errors and increase efficiency. Figure 9 shows schematic diagramme for product optimisation flow. 6.9.3 Conclusion The application of APC and machine learning models optimises plant control and settings automatically to minimise ethane leakage and maximise profits.

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Figure 9. Product optimisation flow

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6.10 Optimisation of energy usage (use case number 10 in Table 1) 6.10.1 General In the past, most company XY plants have adapted Advanced Process Control (APC) system, which provided the plant with some process-related prediction capability to anticipate the changes that happened in the plant process. The company took proactive steps to combat those changes by means of adjusting the process parameters; however, this was unfortunately limited to the process control. This use case applies big data analytics and machine learning application in improving and automating certain aspects of day-to-day operations. 6.10.2 Narrative of use case Energy is extensively used in chemical processing plants either to heat up a process stream, or to cool it down. With the ever rising cost of energy in addition to environmental concern on the importance of energy conservation, it is of a paramount focus to optimise the energy usage. Energy integration study in chemical processing plants has been incorporated at the early design stage, however as the plant ages, the heat exchangers performance deteriorate, making the heat integration less effective and causing energy loss to the surroundings. Historical real-time data from sensors connected to these heat exchangers stored in plant database were studied, filtered and feed into machine learning models to predict the best plant setting for energy optimisation. The model also are able to detect sources of energy leakage and raise the alarm to operators to either change plant setting or to conduct repair and maintenance of the effected equipment or process unit. Notification of these alerts were sent directly to mobile devices of plant manager, shift superintendent and supervisors to ensure that they are all receive the information first hand and can quickly make decisions in terms of the energy optimisation effort. Figure 10 shows schematic diagramme for energy optimisation flow. 6.10.3 Conclusion The combination of big data analytics and artificial intelligence, coupled with IoT application makes the energy optimisation effort more efficient, systematic and easily accessible to key plant personnel. It also encourages continuous monitoring which increases the success rate of reducing energy wastage.

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6.11 HSE for transport safety (use case number 11 in Table 1) 6.11.1 General HSE has always been the top priority of company XY operations. Slogans such as “I am HSE” and “HSE is Everyone’s Business” are commonly heard in any company XY meetings to remind every staff that it is everyone’s responsibility to work safely and to ensure others to work safely as well. Although HSE culture in company XY chemical processing plants are religiously observed, safety behaviour of transporters and drivers (e.g. lorry operators) beyond company XY plant area when transporting hazardous chemicals are sometimes in question. Company XY believes that the HSE culture should also be inculcated to transporters and drivers especially when there is no one around to monitor their actions. 6.11.2 Narrative of use case Special cameras and sensors are installed onto the chemical trucks and lorries which are able to track the driving behaviour and pattern of the drivers. The data collected from these devices are sent to the cloud in which machine learning models for the Transport Safety application reside. The models will then predict the drivers’ behaviour as an outcome, for example if the drivers are sleepy, habitually going over the speed limit or if his driving style might cause an accident; this prediction will then be sent to company XY control centre as alarm and alerts to mobile devices of designated personnel so appropriate actions could be taken to prevent catastrophic chemical transport related accidents. Figure 11 shows schematic diagramme for HSE for transport safety. 6.11.3 Conclusion The application of data analytics, machine learning model and IoT is able to improve company XY transport safety, reduce chances of major road accidents and improve company XY HSE image as a responsible chemical producer and transporter.

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Annex A (informative)

Industry 4.0 fundamental technologies

A.1 Internet of Things (IoT) IoT is defined as convergence of multiple technologies, real-time analytics, machine learning, commodity sensors, and embedded systems. The IoT can realise the seamless integration of various manufacturing devices equipped with sensing, identification, processing, communication, actuation and networking capabilities. The IoT intelligent systems enable rapid manufacturing of new products, dynamic response to product demands, and real-time optimisation of manufacturing production and supply chain networks, by networking machinery, sensors and control systems together. Digital control systems to automate process controls, operator tools and service information systems to optimise plant safety and security are within the purview of the IoT. But it also extends itself to asset management via predictive maintenance, statistical evaluation, and measurements to maximise reliability.

There are many technologies that enable the IoT. Crucial to the field is the network used to communicate between devices of an IoT installation, a role that several wireless or wired technologies may fulfil.

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A.2 Big data analytics Big data analytics is a field that treats ways to analyse, systematically extracts information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualisation, querying, updating, information privacy and data source. Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. Big data can be described by the following characteristics: volume, variety, velocity and veracity.

A.3 Cybersecurity Cybersecurity is defined as protection of computer systems from theft or damage to their hardware, software or electronic data, as well as from disruption or misdirection of the services they provide. Computer system threats can typically be classified into these categories: backdoor, denial-of-service attacks, direct-access attacks, eavesdropping, multi-vector, polymorphic attacks, phishing, privilege escalation, social engineering, spoofing and tampering. Cybersecurity standards are techniques generally set forth in published materials that attempt to protect the cyber environment of a user or organisation. These published materials consist of collections of tools, policies, security concepts, security safeguards, guidelines, risk management approaches, actions, training, best practices, assurance and technologies.

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A.4 Cloud computing The on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. Clouds may be limited to a single organisation (enterprise clouds), be available to many organisations (public cloud), or a combination of both (hybrid cloud). Companies can leverage cloud-based product design, simulation, AI and big data solutions to improve their production processes and build customised products.

The types of cloud services include: a) SAAS - a software that is available via a third-party over the internet; b) PAAS - hardware and software tools available over the internet; and c) IAAS - cloud-based services, pay-as-you-go for services such as storage, networking

and virtualisation. [Source: www.bigcommerce.com]

A.5 Additive manufacturing (AM) Additive manufacturing is a technical term describing technologies of building complex 3D

geometrics from 3D computer images by adding layer-by-layer material until the final desired

3D images are constructed.

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The material can be plastic, metal, concrete or special material such as polymer or food. 3D

printing includes technologies like selective laser sintering (SLS), fused deposition modelling

(FDM), laser-assisted bioprinting, micro-extrusion, etc. 3D food printing is food layer

manufacturing using fused deposition modelling (FDM) method.

The term AM encompasses many technologies including subsets like 3D Printing, Rapid Prototyping (RP), Direct Digital Manufacturing (DDM), layered manufacturing and additive fabrication. [Source: http://additivemanufacturing.com/basics/]

A.6 Artificial intelligence (AI) Machines that mimic ‘cognitive’ functions that humans associate with other human minds, such as ‘learning’ and ‘problem solving’. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, intelligent routing in content delivery networks and military simulations. AI can be classified into five main areas - search, pattern recognition, learning, planning, induction. A more elaborate definition characterises AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation”. AI often revolves around the use of algorithms. Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics (strategies, or "rules of thumb", that have worked well in the past), or can themselves write other algorithms.

A.7 Advanced materials New materials and nano-structures are developed, allowing for beneficial material properties, e.g. shape retention and thermoelectric efficiency. Use of advanced materials allows for massive customisation and development of products.

A.8 Simulation Approximate imitation of the operation of a process or system; the act of simulating firstly requires that a model is developed. This model is a well-defined description of the simulated subject, and represents its key characteristics, such as its behaviour, functions and abstract or physical properties. The model represents the system itself, whereas the simulation represents its operation over time.

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With the use of augmented reality, AI and big data, it is possible to simulate manufacturing processes using different production settings to find the optimal way to manufacture product. Simulation can also be used to test product usage under different operating environment using different types of materials.

A.9 Augmented reality An interactive experience of a real-world environment where the objects that reside in the real-world are enhanced by computer-generated perceptual information, sometimes across multiple sensory modalities, including visual, auditory, haptic, somatosensory and olfactory. Augmented reality alters one's ongoing perception of a real-world environment, whereas virtual reality completely replaces the user's real-world environment with a simulated one. The primary value of augmented reality is derived from the manner in which components of the digital world blend into a person's perception of the real world, not as a simple display of data, but through the integration of immersive sensations, which are perceived as natural parts of an environment. AR allows industrial designers to experience a product's design and operation before completion.

A.10 Autonomous robots A robot intended to physically interact with humans in a shared workspace. Autonomous robots can have many roles – from autonomous robots capable of working together with humans in an office environment that can ask you for help, to industrial robots having their protective guards removed. The EN ISO 10218 Parts 1 and 2 and ISO/TS 15066 specification define the safety requirements for collaborative robots.

A.11 System integration The process of bringing together the component sub-systems into one system (an aggregation of subsystems cooperating so that the system is able to deliver the overarching functionality) and ensuring that the subsystems function together as a system. The system integrator integrates discrete systems utilising a variety of techniques such as computer networking, enterprise application integration, business process management or manual programming. Vertical integration is the process of integrating subsystems according to their functionality by creating functional entities also referred to as silos. The benefit of this method is that the integration is performed quickly and involves only the necessary vendors, therefore, this method is cheaper in the short term.

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NOTE. Vertical integration in Industry 4.0 aims to tie together all logical layers within the organisation from the field layer (i.e.: the production floor) up through R&D, quality assurance, product management, IT, sales and marketing, and so on.

[Source: https://www.manufacturing.net/article/2019/04/horizontal-and-vertical-integration-industry-40]

Horizontal integration or Enterprise Service Bus (ESB) is an integration method in which a specialised subsystem is dedicated to communication between other subsystems. This allows cutting the number of connections (interfaces) to only one per subsystem which will connect directly to the ESB. The ESB is capable of translating the interface into another interface. This allows cutting the costs of integration and provides extreme flexibility. NOTE. Horizontal integration in Industry 4.0 envisions connected networks of cyber-physical and enterprise systems that introduce unprecedented levels of automation, flexibility and operational efficiency into production processes. This horizontal integration takes place at several levels – on the production floor, across multiple production facilities and across the entire supply chain.

[Source: https://www.manufacturing.net/article/2019/04/horizontal-and-vertical-integration-industry-40]

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Bibliography BS ISO 14813-5:2010, Intelligent transport systems - Reference model architecture(s) for the ITS sector - Part 5: Requirements for architecture description in ITS standards EN ISO 10218-1, Robots and robotic devices. Safety requirements for industrial robots. Robots EN ISO 10218-2, Robots and robotic devices. Safety requirements for industrial robots. Robot systems and integration ISO/TS 15066, Robots and robotic devices - Collaborative robots PD ISO/IEC TR 22417:2017, Information technology - Internet of Things (IoT) - IoT use cases PD ISO/IEC TR 20547-2: 2018, Information technology - Big data reference architecture - Part 2: Use cases and derived requirements Design and Methodology of Automated Guided Vehicle-A Review, Suman Kumar Das, M.K.Pasan

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© SIRIM Berhad 2020 - All rights reserved

Acknowledgements

SIRIM Berhad would like to thank the members of the Project Committee on I4.0 - Industry Application/Use Case who have contributed their ideas, time and expertise in the development of this standard. Dr Halim Shah Dato' Hamzah (Chairman) Razak School of UTM in Engineering

and Advanced Technology/RESPECT Business and Advanced Technology Solutions Sdn Bhd

Ms Noraslina Mat Zain/ SIRIM STS Sdn Bhd Ms Zulaikah Zulkifly (Technical Secretary) Mr Thirugnanasambandam Rajagiri Hexa Food Sdn Bhd Mr Eng Chew Hian Huawei (M) Technologies Sdn Bhd Dr Aida Hamimi Ibrahim/ Malaysian Agricultural Research and Dr Masniza Sairi/Ms Sharifah Hafiza Mohd Ramli Development Institute Mr Wong Wai Tong MIMOS Berhad Mr Zaid Zolkifly PETRONAS Mr Syed Kamarul Hisham Syed Ahmad RESPECT Business and Advanced Technology Solutions Sdn Bhd Mr Shamsul Zakaria SIRIM Berhad (SIRIM Industrial

Research) Dr Noraishah Shamsuddin SIRIM Berhad (SIRIM IC Innovation) Ms Siti Musalmah MD Ibrahim SIRIM Berhad (Machine Technology

Centre) Ms Norfaizah Nasir SIRIM STS Sdn Bhd

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© Copyright 2020 All rights reserved. Unless otherwise specified, no part of this standard may be reproduced or utilised in any form or by any means, electronic or mechanical, including photocopying, recording or otherwise, without prior written permission from SIRIM Berhad.

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