Soft Sensor Design for Biodiesel Concentration in a Transesterification Reactor

Biofuels production is facing new challenges every day, related to better process control and quality monitoring. It is very important for the sustainability of these processes to implement strategies and alternatives in order to achieve a continuous production process and to control significant variables involved in the reaction. One of the most difficult variables to measure is the actual Biodiesel concentration inside the reactor. Neural networks have become a useful strategy to give solutions to complex problems; its application is growing faster at industries due to the inherent nonlinear behavior of the processes, modeled easily by this computational tool. The capacity of mapping a complex behavior trough input and output process data, without a complicated and hardly to obtain mathematical model, makes neural networks an attractive strategy to be implemented in most industries, in a soft sensor or a process model scheme. This investigation addresses the need to predict the concentrations of esters (biodiesel) when different triglycerides are reacting with alcohol. Concentration was estimated using an approach that uses a soft sensor that captures the dynamics of these variables through off line laboratory experiments. The soft sensor is actually a Random Activation Weight Neural Net (RAWN), which is a back propagation neural network with a fast training algorithm that does not need any iteration. Also, to reduce the complexity of the soft sensor an optimization procedure was carried out to determine the optimum number of neurons in the hidden layer. In this research Biodiesel was produced by transesterification of palm oil with ethanol and KOH as catalyst. During transesterification reaction the estimation of concentrations is determined by laboratory analysis at off line stages, these variables are very important to control the continuous process of a biodiesel plant.