## 4 Conceptual Framework for Augmented Reality Application
### Data Collection, Geometric and Process Data  
After completing the planning phase, the subsequent step involves data acquisition, wherein information pertaining to the object to be implemented, be it a single component or an entire system, is collected. This data acquisition process encompasses both geometric and non-geometric data. Geometric data, fundamental for generating 3D geometric models,are acquired through various techniques, either in isolation or integrated together. Among the technologies utilized for geometric data acquisition, 3D scanning, particularly 3D laser scanning, is a prominent method in engineering applications. The 3D laser scanning technique involves emitting pulsed or phased laser beams, primarily in the visible or near-infrared spectrum, and rapidly rotating or oscillating mirrors within the scanner to gather data from the object. The resulting dataset, known as a \"scan\" or dot coverage, provides detailed information about the physical contours and dimensions of the scanned object. The appeal of 3D laser scanning lies in its ability to rapidly and accurately capture object measurements, making it highly favored in engineering contexts [47]. Collecting process data for constructing a digital twin is crucial in the chemical industry. This practice involves the systematic acquisition and recording of relevant information throughout all stages of production, from the synthesis of chemical substances to storage and distribution processes, to create a precise and real-time virtual representation of the entire production process. Data collection plays a crucial role in building the \"digital twin\" in augmented reality, and it is carried out through various sources, such as sensors, monitoring systems, and historical records. The quality and quantity of this information are essential to ensure the accuracy and reliability of the virtual representation. These data provide the foundation for creating a detailed and precise replica of the physical object or system, allowing real-time simulations and analysis. To achieve this, data related to production scheduling, such as timelines, task sequencing, and resource allocation, are collected. Additionally, information regarding production capacity and equipment availability is recorded. Operational data from machinery and equipment, such as speed, temperature, pressure, and production levels, is monitored to monitor the performance of chemical processes. IoT sensors in the facilities allow real-time data collection, providing a detailed view of the operations. Another essential aspect is collecting information about the sequence of steps in the production process, including execution times and interdependencies between activities. These data are essential for understanding workflow and identifying potential bottlenecks or optimization opportunities in the production chain. In addition to the data mentioned above, various other types of information are collected in the chemical industry for the construction of the augmented reality digital twin:  
* Chemical composition: detailed data on each product's chemical formula and composition, including component proportions and purity specifications. * Chemical reaction parameters: data related to temperatures, pressures, reaction speeds, and other specific parameters that affect the outcome of chemical reactions. * Quality control: information about quality tests conducted during production ensures that products meet standards and specifications. * Raw material consumption: data on the quantity of raw materials used in each production stage and the material stock level. * Emissions and environmental control: information about energy consumption, gas emissions, waste treatment, and other practices related to environmental control. * Equipment condition monitoring: data on the health and integrity of equipment, such as piping systems, tanks, and reactors, to prevent failures and ensure safety.