[EN] Sewage and landfill reciprocating gas engines have proven to be a reliable energy power generation system when a proper
maintenance schedule is done. The intrinsic nature of the stroke mechanism that creates parts movements and reciprocating
masses supposes a challenge in order to improve maintenances schedules, reliability and availability. In the case of sewage and
landfill gases that could not be properly cleaned, the engine reliability is more concerned. Asset monitoring systems that monitor
and creates alerts when some variables are out of some limits are highly desirable. In this paper authors show how to create a
virtual twin model from real data that could be optimized and compared in real time with working variables of the actual engines
in order to predict future maintenance schedules and increase the engine availability. In sewage and landfill reciprocating gas
engines this could have a positive impact of increasing the reliability and due to this, also the customer satisfaction. An accurate
computational model is created taking some inputs and outputs to use it in an online cloud asset monitoring system so that it
could be used as an extra supervisor agent
