Gas Oil Color (ASTM) inference with Neural Network in an oil refinery distillation column

RAMIRO VILLAMIL; GERARDO DE VINCENTI; NESTOR TUMINI ; FAVIO MASSON; OSVALDO AGAMENNONI

Buenos Aires

Conferencia; 7th Latin American and Caribbean Petroleum Engineering Conference; 2001

The first operation in an oil refinery is the atmospheric distillation. To maximize the extraction of some products like Gas Oil, a proper set of on line analysis instruments is required. These kinds of instruments are not always available, especially in medium and small size processing plants. Due to the fact that color is a limiting specification, it constitutes a restriction for production optimization. Availability in real time of a good value estimate is what allows work to be carried out permanently in operative conditions where the process is most beneficial. In this work a Neural Network (NN) approach to infer the color is proposed. A feed forward NN structure is used to identify the non-linear mapping from available process variables to that property. To acquire representative I/O data, a set of dynamic experiments (move test) was developed in the plant. After that, a rigorous analysis to select the set of input variables was performed. In this study, process engineer?s knowledge as well as some mathematical tools were used to evaluate a minimum set of inputs. From this analysis, the set of forty-three available inputs is reduced to the eight most sensitive with respect to the color representation. Furthermore, rather than represent the entire transformation from the set of inputs variables to the output variable by a single neural network function, we analyze the possibility of breaking down the mapping into an initial pre- processing stage followed by a parameterized neural network model.