Thermal power plants, TPP, are one of the main players in the phosphoric acid and fertilizer production value chain. The control of power plant assets involves considerable complexity and is subject to several constraints, affecting the asset’s reliability and, most importantly, plant operators’ safety. The main focus of this paper is to investigate the potential of an agent-based digital twin architecture for collaborative prognostic of power plants. Based on the ISO 13374:2015 scheme for smart condition monitoring, the proposed architecture consists of a collaborative prognostics system governed by several smart DT agents connected to both physical and virtual environments. In order to apprehend the potential of the developed agent-based architecture, experiments on the architecture are conducted in a real industrial environment. We show throughout the paper that our proposed architecture is robust and reproduces TPP static and dynamic behavior and can contribute to the smart monitoring of the plant in case of critical conditions.

Tubular exchangers are among the most widely used exchangers in the industrial sector. It is essentially divided into two most common categories: shell and tube heat exchangers (STHE) and coaxial exchangers. It is also possible to consider a STHE as a sum of simple coaxial tubes, which greatly facilitates the understanding and modeling of STHE. Currently, the optimization of heat transfer in exchanger is a recurring problem for thermal engineers. Therefore, several numerical and experimental studies have been carried out. In this paper, the experimental and numerical study of heat transfer and dynamic behavior analysis of tubular heat exchanger (HE) is carried out in order to optimize the energetic performance of the exchanger. Experimentally, two configurations of co-current and counter-current exchangers have been investigated. Theoretically, the dynamic modeling and simulation of the HE was performed using Matlab/Simulink, Unisim-Design simulator, ANSYS-Fluent and Comsol multiphysics commercial software. The models' results were validated experimentally with very good agreement of 99%. Finally, the developed models are confronted to an industrial case of sulfuric acid cooling unit. The results obtained have a very good accuracy of 98.99% with the real plant data.

Sulfuric acid is a basic chemical used intensively in many industrial processes. To produce the sulfuric acid, there are two major processes: the lead chamber process and the current contact process. The first step in the latter process consists of burning sulfur in the air to form sulfur dioxide. In this paper, modeling and simulation of an industrial sulfur burning unit are performed. For the modeling task, an adiabatic conversion reactor was used for the sulfur burner, a centrifugal pump for the liquid sulfur pump, and an air compressor for the air supply. To simulate the model, the Unisim Design R451 simulator was used. Then, the simulation results were validated using industrial data. In addition, a parametric study was performed to analyze the effects of the inputs' variations on the sulfur burner outlet. The proposed model can be used to develop a digital twin of the studied sulfur burner to perform real-time simulations and test new control and optimization strategies.

In industrial smelters, sulfuric acid is manufactured using the elemental sulfur in a series of three-unit operations: elemental sulfur oxidation, sulfur dioxide catalytic conversion, and sulfur trioxide absorption. The sulfur oxidation, which is the basic step in this process, is generally performed under a sulfur combustion furnace that ensures the production of the process gas stream, which will be the main supply stream to the other unit operations. In this paper, a dynamic model is developed based on the fundamental mass and energy balance, including the sulfur oxidation and the dynamic flow behavior aspects within the furnace. The obtained model is simulated in the Matlab/Simulink environment and data from an industrial plant were used to validate the model. The simulation results and the plant measurement comparison showed an accuracy of 96%, with a mean absolute error of 16.12 °C and a root mean square error of 23.27 °C. Afterwards, the effect of different operating conditions and disturbance parameters on the sulfur combustion furnace performance were studied. Finally, the relationship and a correlation between the temperature and sulfur dioxide molar fraction at the outlet of the furnace were investigated for industrial use.