This study presents a methodology based on the process integration techniques and multiple representation of heating and cooling requirement concept to improve the energy efficiency of a large-scale chemical plant. Considering the difficulties of data gathering in a large-scale plant, a multi-layer analysis including five levels of detail for defining energy requirement are introduced and the practice of applying the combination of these levels rather than a unique one is demonstrated. The methodology begins by generating the composite curve with the utility representation of the energy requirements. Based on the available level of the data, the composite curve is systematically improved by upgrading from the utility representation to the technological or thermodynamic ones. The single process integration (SPI) and total site integration (TSI) is performed and indicates considerable potential of energy saving. This potential has been further improved by either process condition modification or with the integration of mechanical vapour recompression (MVR) and heat pump. The Suitable energy conversion units are integrated and optimized by minimizing the energy requirement cost using the mixed integer linear programing (MILP). The optimized site utility integration increases the energy saving potential of the base-case system by 55%. A multi-objective optimization with evolutionary algorithm (EMOO) is performed to find the optimum combination of units with different representations by minimizing the operating cost and maximizing the number of utility represented units. The analysis of results shows that the effort for process modification of actual configuration of the site as well as the number of units that requires detailed thermodynamic data analysis can be narrowed by using a combination of different representations. Application of the proposed methodology is demonstrated through an industrial case study highlighting the different steps and the potential of this approach.