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Verification techniques express program states as logical formulas over program variables. For example, symbolic execution and abstract interpretation encode program states as a set of linear integer inequalities. However, for real-world programs these formulas tend to become large, which affects scalability of analyses. To address this problem, researchers developed complementary approaches which either remove redundant inequalities or extract a subset of inequalities sufficient for specific reasoning, i.e., formula slicing. For arbitrary linear integer inequalities, such reduction approaches either have high complexities or over-approximate. However, efficiency and precision of these approaches can be improved for a restricted type of logical formulas used in relational numerical abstract domains. While previous work investigated custom efficient redundant inequality elimination for Zones states, our work examines custom semantic slicing algorithms that identify a minimal set of changed inequalities in Zones states