Neuro-Fuzzy Digital Twin of a High Temperature Generator


Abstract:

Solar absorption plants are renewable energy systems with a special advantage: the cooling demand follows the solar energy source. The problem is that this plant presents solar intermittency, phenomenological complexity, and nonlinearities. That results in a challenge for control and energy management. In this context, this paper develops a Digital Twin of an absorption chiller High Temperature Generator (HTG) seeking accuracy and low computational efort for control and management purposes. A neuro-fuzzy technique is applied to describe HTG, internal Lithium-Bromide temperature, and water outlet temperature. Two Adaptative Neuro-Fuzzy Inference Systems (ANFIS) are trained considering real data of eight days of operation. Then, the obtained model is validated considering two days of real data. The validation shows a RMSE of 1.65e-2for the internal normalized temperature, and 2.05e-2for the outlet normalized temperature. Therefore, the obtained Digital Twin presents a good performance capturing the dynamics of the HTG with adaptive capabilities considering that each day can update the learning step.

Año de publicación:

2022

Keywords:

  • solar energy
  • Fresnel Solar Collector
  • Absorption Chiller
  • Lithium-Bromide
  • ANFIS
  • High Pressure Generator

Fuente:

scopusscopus

Tipo de documento:

Conference Object

Estado:

Acceso abierto

Áreas de conocimiento:

  • Inteligencia artificial

Áreas temáticas:

  • Métodos informáticos especiales
  • Administración de la Iglesia local
  • Física aplicada