The process of manufacturing nitric acid, the main building block in the manufacturing of explosives and fertilizer, is one of the main emitters of NOx gases. NOx is a key contributor to global warming, Typical approaches to operational improvement in the chemical manufacturing industry are cost intensive, with environmental implications. Majority of publications on optimization of chemical processes use expensive design and simulation software or a physical trial. The study adopts a process centric optimization approach for a nitric acid manufacturing process. Real time and historic process data is used to develop regression models for forecasting process impact in terms of energy use, environmental emissions and production outputs. Four regression models are developed: NO2 production, nitric acid production, total NOx emissions and the energy balance of the manufacturing process compressor train. The robustness of the four models is compared to the virtual process; overall, the average error of the combined models is 17.5% with the energy balance model proving to be the most robust. Applying the regression models allows the organization to optimize operations while adhering to environmental regulations and maintaining commercial competitiveness.