The problem of ambiguities has presented an obstacle to the practical use of qualitative simulation in dealing with real-life dynamic systems. One of the dominant causes of these prevailing ambiguities is the parsimonious use of information by current qualitative simulation models. Humans seem to utilize more quantity information to produce less ambiguous predictions when reasoning on dynamic mechanisms. To cover such human algebraic reasoning, the representation and processing of quantity information should be extended. This paper presents an inference system specialized for processing quantity information in order to support qualitative simulation. The system stores the information in the form of constraints into a quantity base, a notion that is parallel to the symbolic fact base, and processes queries based on linear programming techniques and goal-tree search. Temporal reasoning is also facilitated through an improved utilization of derivative information.