A Practical Approach to Energy Optimization Using Pinch Analysis: A Case Study of an Oil Refinery

Abstract

The objective of this paper is to perform an energy optimization study using pinch analysis on the Heat Exchanger Network (HEN) of a Crude Distillation Unit to maximum heat recovery, minimize energy consumption and increase refining margin. The heat exchanger network (HEN) considered comprises exchangers from the pre-heat section of the atmospheric distillation unit, which recovers heat from the product streams to incrementally heat the crude oil feed stream before entering the furnace. This paper illustrates how to perform a detailed HEN retrofitting study using an established design method known as Pinch Analysis to reduce the operating cost by increasing energy savings of the HEN of an existing complex refinery of moderate capacity. Analysis and optimization were carried out on the HEN of the CDU consist a total of 19 heat exchangers which include: process to process (P2P) heat exchangers, heaters and coolers. In the analysis, different feasible retrofit scenarios were generated using the pinch analysis approach. The retrofit designs included the addition of new heat exchangers, rearrangement of heat exchanger (re-sequencing) and re-piping of existing exchangers. Aspen Hysys V9 was used to simulate the CDU and Aspen Energy Analyser was used to perform pinch analysis on the HEN of the pre-heat train. Several retrofit scenarios were generated, the optimum retrofit solution was a trade-off between the capital cost of increasing heat exchanger surface area, payback time, energy / operating cost savings of hot and cold utilities. Results indicated that by rearrangement (Re-sequencing), the pre-heat train can reduce hot (fired heat) and cold (air and cooling water) utilities consumption to improve energy savings by 8% which includes savings on fired heat of about 4.6 MW for a payback period of 2 years on capital investment. The results generated were based on a ΔTmin of 10°C and pinch temperature of 46.3°C. Initial sensitivity analysis on the ΔTmin indicated that variation of total cost index is quite sensitive and increases with increase in ΔTmin at the temperature range of 14.5-30°C, however total cost index remains constant and minimal at a temperature range between 10°C-14.5°C for the CDU preheat train under study. In addition, the implementation of the optimum retrofit result is straightforward and feasible with minimum changes to the existing base case/design.