performance improvement

Analyzing and improving business processes through process mining techniques has become one of the most important management tools in the digital age. These techniques help to identify the problems and inefficiencies in the processes by analyzing the data recorded in the information systems. By using methods such as process discovery, compliance review and process improvement, organizations are able to optimize their processes and improve overall performance based on data. This approach helps process management to make better decisions and make significant improvements in the efficiency and quality of their services. The purpose of this research is to analyze and improve business processes through process mining techniques. In this research, CRISP methodology has been used as the main data mining framework to analyze the data in the enterprise resource planning system. CRISP methodology is a standard model in data mining that includes six main steps: business understanding, data understanding, data preparation, modeling, evaluation and deployment, and by modeling similar projects, business goals are modified based on past experiences. This process may require the collection of new data or the modification of existing data. The result of these steps is a list of data mining projects with specific information and timelines. The results of this research show that process mining techniques, including process discovery, compliance review and process improvement, help business managers to improve the performance of organizations using real data. optimize These techniques have the ability to identify inefficiencies and deviations and can lead to reducing costs, increasing speed, improving service quality and customer experience. Crisp methodology with its six steps helps in data analysis and decision-making, and its implementation projects show the importance of these techniques in improving processes and increasing the productivity of organizations.

Industrial waste incinerators in gas refineries or sulfur recovery units are responsible for eliminating the waste gases such as hydrogen sulfide (H2S) and carbon dioxide (CO2) produced in the process. Most of these units are facing problems such as incomplete combustion, lack of proper control systems for measurement, and the presence of pollutants such as carbon monoxide and nitrogen oxides, etc. Therefore, in this research, to improve the performance of the waste incinerator of Fajr Jam Refinery, furnace modeling was done by computational fluid dynamics (CFD). The software Fluent 6.3 is used for the modeling of the incinerator. To achieve this goal, parameters such as the ratio of fuel to air in the inlet duct of main air, the amount and role of the additional air inlet at the bottom of the chimney, the installation of the additional air inlet duct at the end of the furnace for mixing more oxygen and H2S, air injection into the inlet duct of acid gas and the feasibility of preheating the incoming air and its effect on the combustion efficiency was investigated. The modeling results showed that increasing the airflow can reduce the amount of CO and H2S, but the amount of NOx compounds increases. To establish a balance between the output of pollutants from the furnace, solutions to improve the unit performance were presented, and the appropriate flow range of excess air in the furnace was determined.
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