Hydrogen management represents one of the significant, crucial aspects of refineries. It optimizes hydrogen production, consumption, and recovery. While hydrogen network optimization fundamentally aims to reduce the fresh hydrogen makeup and off-gas discharge flowrates, in addition to maximizing the hydrogen recovery through off-gas purification techniques. It is viewed as a complex procedure compared to other techniques, defining possible investment options to reuse hydrogen before it reaches the fuel station. Due to hydrogen production excessive high costs, minimizing hydrogen production through refining is considered as a remarkable potential for economic profits of any refinery.
Hydrogen Network Optimization
One of the studies tackling in-depth hydrogen management is: Hydrogen Network Optimization for MIDOR and MOSTOROD Egyptian Refineries. An empirical study published in the International Journal of Artificial Intelligence and Mechatronics (IJAIM), Vol. 4 No. 4; on January 2016, by Abeer M. Shoaib, Ahmed A. Bhran, two faculty staff members at Faculty of Petroleum and Mining Engineering, Suez University at Petroleum Refining and Petrochemical Engineering Department as well as Mohamed I. Seddik, a Refinery Operations Section Head (DCU, Hydro-processing, SRU), Egypt.
The main objective behind the study is to present an efficient procedure for optimum hydrogen network for two Egyptian refineries. The paper introduces comprehensive information on the use of fresh hydrogen, the discharge of unused amounts, and the ultimate extent of mass integration among process streams (sources) and units (sinks). The two purification units used were: PSA and Membrane, which have been applied to select the best stream to be purified.
Previous Designed Techniques
In the earlier previous years, numerous designs techniques have been created to decrease the fresh resources usage such as hydrogen using network synthesis and analysis. For instance, firstly, mass exchange networks (MENs) were used. However, there were a lot of limitations and problems accompanied by using MENs. Instead, a source (supply) sink (demand) representation took place, where it can be applied for all fresh resources. Other methods came next to supply/sink representation having some drawbacks. Furthermore, similar to some Mathematical programming techniques designed, the Gas Cascade Analysis (GCA) was created lately.
Approaches, Assumptions, and Statistical Techniques
The paper’s methodology can be principally concentrated in three major steps: Identifying the problem statement; setting approaches and assumptions and the Optimization formulation (which depends on previous research and cost calculations); analyzing and applying these steps on two case studies. In this study, the major focus is to target the network and to select the best source. The combined technique is applied to two Egyptian refineries to verify the effectiveness of the method.
As a first step, it is essential to target the problem without choosing purification units; where sinks and sources satisfy certain constraint or equation. Each source has a given flow rate (Wi) and a given composition (yi) and each sink needs a given flow rate (Gj) and a composition (Jz).
In the second step, the required hydrogen amount will be estimated to be purified. As per the study, some assumptions were determined, displayed and highlighted in: any source could be sent to the purifier, according to its flowrates and purity. In addition, there are two used purifiers as referred before (PSA and Membrane) where each purifier has one input and two outputs and finally it is considered that fuel gas is a mixture of hydrogen and methane.
The third step includes 2 phases: the purifier selection phase and Optimization formulation and cost calculations. After choosing the best purifier, the optimum network is drawn with the best purifier from the results of the optimization program. The optimization formulation is based on the source/sink allocation. The optimization program used is Lingo software version220.127.116.11. “Cost Calculations” phase is to estimate costs and represent them in equations introduced in a linear program. The total annual cost is a summation of the operating costs and the annual capital costs. Illustrating that the capital cost is composed of the new compressors’ costs, new purifiers and piping costs. While the operating cost of a hydrogen system is composed of hydrogen utility costs, purifiers’ operating cost, the cost of electricity used in compressors minus the value of the gas sent to site fuel system. It is worth noting that the best stream to be purified is the stream giving the lowest annual operating cost.
Results and Outputs
Case Study 1: ERC Refinery Results
By applying the Lingo optimization to ERC refinery H2 network without new purifiers, the minimum fresh H2 and the minimum discharge flow rate were reduced from 980 to 911 kilomole per hour (kmol/hr) and from 600 to 576 kmol/hr, respectively. After adding membrane and PSA purifiers, the optimization results show that the optimum network was obtained after the purification of H2 source 4 (shift converter stream) with the two purifiers. Using membrane as a purifier reduced fresh H2 flow rate to 861 kmol/hr and discharge flow rate to 526 kmol/hr. While by using PSA purifier, fresh H2 flow rate decreased to 704 kmol/hr and discharge flow rate decreased to 365.3 kmol/hr. The saving in fresh hydrogen is 28.5 % (279.6 kmol/hr) and saving in discharge is 39 % (235 kmol/hr) and the saving in operating cost was calculated to be 3.085*106 $/year. Accordingly, the optimization results for ERC refinery H2 network with the addition of PSA purifier show the optimal network.
Case Study 2: MIDOR Refinery Results
By applying the Lingo optimization to MIDOR refinery H2 network without new purifiers, the minimum fresh H2 and the minimum discharge flow rate were reduced from 2888 to 2277 and from 1472.63 to 1084 kmol/hr respectively. After adding membrane purifier, fresh H2 flow rate decreased to 2275 kmol/hr and the discharge flow rate decreased to 872 kmol/hr. While adding PSA purifier instead, fresh H2 flow rate decreased to 1889 kmol/hr and discharge flow rate decreased to 629 kmol/hr. This optimal network showed a saving in fresh hydrogen and discharge by 34 and 57 % respectively. The reduction of the network operating cost is calculated to be 11.2 million $/year. The optimal H2 network is obtained with the addition of PSA purifier rather than the addition of the membrane purifier for MIDOR refinery as in ERC refinery.
Generally, after defining the optimized hydrogen network for the two selected refineries, the study results show that the hydrogen operating costs decreased. In the two displayed refineries, adding PSA purifier was the optimal choice. However, it is not a general rule for other refineries. This paper clarifies the importance of using purifiers and work on hydrogen network optimization in refining processes.