Optimization of a mechanical peat dewatering process by using multivariate data analysis

Abstract

A new experimental design (nearly orthogonal experimental design) was used for a dewatering study under laboratory conditions with two type of peats. This design was able to test more levels for each design variable with considerably fewer experiments than the usual factorial or fractional factorial designs. The process under study is dependent on many variables and instead of a mechanistic model, an empirical model based on multivariate data analysis was chosen. The way of expressing the relation is by regression coefficients obtained from partial least square regression. For accommodating the possibility of detecting and describing nonlinearities, the design variables were augmented with their square and interaction terms. User-friendly response surface methodology was used to study the relationship between the design and the response variables. Results presented are response surfaces for different response variables. Performing the experiments under the nearly orthogonal experimental design conditions and analysing the data by using a multivariate data analysis system, fundamental mechanisms underlying the mechanically peat dewatering process can be understood. The models generated using the PLS method predicted the outcome from new experiments within reasonable limits. With relatively few experiments, information can be collected to predict the results.