Comparison of the system performancefor liquid refrigeration, controlled cooling the different ways of control

Author(s)

Flávio Vasconcelos da Silva

• Document type: Doctoral Thesis
• Press: Campinas, SP.
• Institution: Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia de Alimentos
• Defense date: 2003-11-13

Examining board members

Vivaldo Silveira Júnior; Jaime Vilela de Resende; Jose Maria Saiz Jarbado; Nelson Luis Cappelli; Luís Augusto Barbosa Cortez; Ana Maria Frattini Fileti; Rubens Maciel Filho

• Advisor: Vivaldo Silveira Júnior

Abstract

A prototype of refrigeration system in a chiller plant was assembled and instrumented for development of a conventional and fuzzy controllers. Fuzzy controls are increasingly being applied to industrial process especially in process with complex mathematical modeling. The capacity of action in a system controlled by a fuzzy logic is based only on a expert knowledge and its capacity to interact with all variables of process. The efficiency in a cycle of refrigeration is directly related to system capacity to maintain the temperatures and pressures values corresponding to process demand. Evaporating and condensing temperatures have a great influence on electrical demand and general performance of the cooling system. Being highly influenced by external disturbs. In a secondary fluid of the chiller (propylene glycol), the temperature control is associated with the product quality to be cooled. Then, evaporating and condensing temperatures were assumed as controlled variables The manipulated variables were: compressor and reciprocating pump rotation frequencies and position of pneumatic control valve. Experiments of dynamic behavior knowledge of the system were done applying the experimental design methodology to evaluate the individuals and interactions effects of the systems variables under disturbances. Control experiments were developed using SISO (Single Input ¿ Single Output) and SIMO (Single Input ¿ Multi Output) strategies. The time-integral performance criteria and electrical energy consume allowed to define the best control loops (AU)