## 1 Introduction  
Automation of industrial processes remains a dominant development area in the modern industry due to its advantages in increasing efficiency, cost savings, consistency, and quality, enhancing security and scalability [1; 2; 3; 4; 5]. The advantages of automation go beyond its direct benefits, including the ability to collect and process data, move to a real-time data-based management model, implement sustainability policies by eliminating production waste, and optimize energy consumption. Industry 4.0 implementation relies heavily on automation, an essential pillar [6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. However, Industry 4.0 was a technological paradigm centered around cyber-physical systems, digital connectivity, and Artificial Intelligence, resulting in increased efficiency [14; 15]. The guidelines and principles of Industry 4.0 were mainly focused on increasing the efficiency of production systems and replacing human labor with connected machines. As the concept grew more widespread, it became clear that it was associated with revolutionary changes, significant investments, and the need for extensive awareness among the teams implementing it. Increasing automation, digitalization, growing machine autonomy, and globalism (in the sphere of production, trade in services, and consumer goods) have necessitated the development of solutions to strengthen the competitiveness of the industry and national economies [16]. Industry 4.0 in its current form has lost value, especially in applications addressing the climate crisis, the global economic situation, and deep social tensions. Comprehensive system transformation was prevented due to the need for more coverage of essential design and action aspects. This hindered resource and material use, decoupling from negative environmental, climate, and societal impacts. In this approach, modern technologies are applied to achieve the desired outcome. Figure 1 shows the main areas developed within the Smart Industry concept [17; 18]. From the end of 2022, the perception of the manufacturing industry has changed with the development of Industry 5.0 [19; 20; 21; 22; 23]. The emphasis on circular economy and sustainability has not diminished the need for advanced industrial process automation systems. Automation remains a leading force in modern industry, driving digital transformation towards sustainability [24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. Automation is gradually adopting many solutions to support pro-ecological development:  
* A shift from the traditional hierarchical automation pyramid model to the connectivity pillar, which necessitates significant digitalization and both horizontal and vertical integration [34; 35; 36; 37; 38; 39; 40; 41] but allows the operation of reliable data,
* Enhancing employee safety and optimizing operational excellence through new control quality methods and technologies [42],
* Inclusion of sustainability--in particular, monitoring machine utilization rates, diagnosing and predicting operating states, sustainable energy management, optimizing processes, and reducing energy consumption [43; 44; 45],
* Production systems can be made more flexible while reducing interruptions and reallocating employees to higher-value tasks, such as optimizing products and processes, developing innovative product and process improvements, and implementing continuous improvement methods [46; 47],
* Sustainable product and process design (supported by simulation methods, Digital Twins, aided by real-time data analysis) [48; 49; 50; 51],
* Integration of production data into a company's business development planning system is a crucial task for inclusive growth and development [52; 53; 54; 55]. * Progressive promotion is implementing digital operator support and a paperless policy [56]. Due to the need to meet the imposed requirements, many state-of-the-art solutions from modern technology and management methods were adapted to automation. Figure 2 shows the primary advancements in industrial process automation.