  There is an exhaustive study around the area of engine design that covers
different methods that try to reduce costs of production and to optimize the
performance of these engines. Mathematical methods based in statistics,
self-organized maps and neural networks reach the best results in these designs
but there exists the problem that configuration of these methods is not an easy
work due the high number of parameters that have to be measured. In this work
we extend an algorithm for computing implications between attributes with
positive and negative values for obtaining the mixed concepts lattice and also
we propose a theoretical method based in these results for engine simulators
adjusting specific and different elements for obtaining optimal engine
configurations.
