A Fuzzy Logic-based Tuning Approach of PID Control for Steam Turbines for Solar Applications

Abstract This work aims at improving the control concept based on PID controller by jointly exploiting experience and knowledge on the system behaviour and artificial intelligence. A Concentrated Solar Power Plant (CSPP) system has been modelled and a stability and performance analysis has been carried out, focusing on power control loop, which is normally based on standard PID. A hybrid fuzzy PID approach is proposed to improve the steam turbine governor action and its performance are compared to the classical PID tuned according to three different approaches. Compared to the classic PID, the PID fuzzy logic controller extends the simplicity of PID and adapts the control action at actual operating condition by providing the system with a sort of "decision-making skill". The possibility to design implementable algorithms on PLC, which have stringent computational speed and memory requirements, has been explicitly taken into account in the developed work

A Brokering-Based Interaction Protocol for Dynamic Resource Allocation in Steel Production Processes

Steel manufacturing involves a series of dynamic processes that require an efficient and effective management of the plant resources. The coordination and the allocation of the resources is an important aspect for ensuring a continuous production flow as well as a good quality of the final products, especially when unexpected events can compromise the overall system performance. This paper presents an agent-based protocol for dynamic resource allocation in order to establish collaboration among agents within steel production processes. The proposed protocol is based on the brokering mechanism and is designed in order to solve the problem of the concurrency, which arises when several agents are interested in using the same resources, and to handle dynamic changes, such as unexpected events that can affect the resource allocation process. Experimental results show how the designed protocol allows the agents coordination by guaranteeing the use of the resources and the correct flow of the production

An Application-Oriented Cyber-Physical Production Optimisation System Architecture for the Steel Industry

The new generation of steelworks shaped by Industry 4.0 are digitized, networked, flexible and adaptable. Production processes use distributed information and communication structures, are more autonomous and capable to react to dynamic evolutions of the environment. Cyber-physical systems are a fundamental component of Industry 4.0 and enable new generation of smart processes. This paper presents a modular architecture approach for the design of cyber-physical steel production processes. The approach is tested within a production facility for long products such as rails or tubes taking into account the main peculiarities of the sector. The use of an industrial-agent-based solution for enabling intelligent capabilities and interactions among cyber-physical modules is investigated and adopted. Experimental results highlight the industrial applicability of the adopted implementation scheme combining agent-based technology with the proper connection between models, communication and optimsation methods

A hybrid peer-to-peer architecture for agent-based steel manufacturing processes

The new generation of steel manufacturing processes shaped by Industry 4.0 are more digitalized, networked, flexible and adaptable. Production processes use distributed information and communication structures, are more autonomous and capable to react to dynamic changings of the environment. Agent-based systems represent a paradigm, which is well suited to address these new generation of smart processes. The paper presents a hybrid peer-to-peer architecture for agent-based steel production processes. The architecture exploits a central database server for storing and retrieving updated information from peers about a cold rolling manufacturing process. The cold rolling process is modeled as a multi-agent system composed of four types of autonomous agents, each playing a different role in the steel production chain. Agents are designed to take autonomous decisions, and to coordinate and collaborate with each other, by ensuring the dynamic plant resources allocation even if unforeseen interruptions of the production flow may happen. The proposed approach is designed for the steel strip manufacturing process but can be easily readapted to any flat production process. The test of the design multi-agent system with the proposed architecture is supported though the simulation of the dynamic plant resources allocation under changing dynamic conditions

Multi-agent systems to improve efficiency in steelworks

A multi-agent system consists of several computational entities capable of autonomous actions, called agents, which communicate with each other, and have the ability to coordinate their actions and to cooperate. Multi-agent systems received a great interest and attention over time, as they can be seen as a key enabling technology for complex applications, where distributed and processing of data, autonomy, and high degree of interactions in dynamic environments are required at the same time. Therefore, in view of current and future developments of the digitalization of industrial production cycles promoted by Industry 4.0, multi-agent systems are foreseen to play an increasing role for industrial production management and optimization. Because of barriers represented by large presence of legacy systems, in the steel sector agent-based technology is not widely applied yet, and multi-agent systems applications are very few. On the other hand, steel manufacturing industries are complex and dynamic systems whose production processes held a strategic role in the global economy. During last decades, the steel sector has undergone relevant transformations, especially through the massive digitalization and the innovation introduced by Industry 4.0. A further evolution is foreseen in the incoming years to improve the sustainability of the production cycle by improving energy and resource efficiency. Therefore, steel industries must face several challenges on the path toward the factory of the future. In such context multi-agent systems, through their intrinsic properties, such as autonomy, social abilities, reactivity, proactivity, and mobility, can overcome existing drawbacks and barriers, by increasing flexibility, improving resources efficiency, handling production operations, reacting to unpredicted events, optimizing production processes, and supporting legacy systems. In this paper, some applications of multi-agent systems in steel sector are presented to show the advantages and opportunities of agent-based technology

Renewable hydrogen production processes for the off-gas valorization in integrated steelworks through hydrogen intensified methane and methanol syntheses

Within integrated steelmaking industries significant research efforts are devoted to the efficient use of resources and the reduction of CO2 emissions. Integrated steelworks consume a considerable quantity of raw materials and produce a high amount of by-products, such as off-gases, currently used for the internal production of heat, steam or electricity. These off-gases can be further valorized as feedstock for methane and methanol syntheses, but their hydrogen content is often inadequate to reach high conversions in synthesis processes. The addition of hydrogen is fundamental and a suitable hydrogen production process must be selected to obtain advantages in process economy and sustainability. This paper presents a comparative analysis of different hydrogen production processes from renewable energy, namely polymer electrolyte membrane electrolysis, solid oxide electrolyze cell electrolysis, and biomass gasification. Aspen Plus® V11-based models were developed, and simulations were conducted for sensitivity analyses to acquire useful information related to the process behavior. Advantages and disadvantages for each considered process were highlighted. In addition, the integration of the analyzed hydrogen production methods with methane and methanol syntheses is analyzed through further Aspen Plus®-based simulations. The pros and cons of the different hydrogen production options coupled with methane and methanol syntheses included in steelmaking industries are analyzed