Journal of Chemical Engineering and Energy Materials

Abstract During six decades of development planning at the level of developing countries, which has been done with different forms and patterns. Development planning has not had a proper place and rural development is mainly based on tourism perspectives or physical functions and has never led to introspection and self-criticism, so that in our country in the last few decades by providing amenities and services to the villages, the process of migration has not decreased, and the provision of facilities without planning for employment and income has increased the expenses of the residents of the rural areas of the country, while the income has remained constant. In this way, rural areas have always played a role related to urban areas as a margin and far from the comprehensive thinking of development. Creating industries is one of the strategies that help the development of villages, the industrialization of rural areas as an accelerator for creating sustainable employment and the most effective solution to solve the problem of rural unemployment. The theory of industrialization and the optimal planning of industries in rural areas are considered as a catalyst for creating sustainable employment and as a last resort to solve the problem of poverty in the potential areas of the same region to solve the problem of unemployment and a soothing factor for disadvantaged rural areas. Come the consequences of the appropriate establishment of rural industries in temperate and humid rural areas, where the amount of agricultural land is decreasing every day, is considered a suitable solution.

Abstract The relationship between industrialization and rural development in the texts of developing countries has different views, so that Rajesh Chandar has emphasized the role of industrialization in the development process by expressing these views and believes that the following views have made the role of industry in development more sensitive. Industrialization in developing countries is known as a historical necessity. In this regard, due to the lack of another provable model for development, historically this claim that development requires industrialization has been accepted. Due to the minimization of the competitiveness of the agricultural sector compared to the industrial sector and the significant reduction in the price of agricultural products compared to industrial products, the trade conditions in the agricultural sector are significantly limited and the tendency towards industrialization as it is an economic fact. Even if the manufacturing and production of goods in the process of industrialization is not considered as a desirable option for the development of the agricultural sector, the trend towards industrial activities can be a source of encouragement for the economic development process as a complement to the agricultural sector.

Abstract In the present study, while identifying potential risks and classifying them according to the Risk Breakdown Structure (RBS), we determine the criteria for identifying and assessing risks qualitatively, and then by converting qualitative criteria into quantitative ones and using the FMEA technique and calculating the Risk Priority Number (RPN) and responding to high-priority risks, we control the obstacles to achieving the project goals. By carrying out this process in the oil processing unit construction project, which was carried out using the EPC method, important and risky risks were identified and controlled based on the project phases, and precise control was exercised to prevent imposing additional and possible costs on the project and creating defects in its other objectives, so that important and influential risks on key project activities were organized in the form of a risk management plan, and their future and possible consequences, which could be one of the most important factors in the failure to comply with the project implementation plan, were minimized. The results of the present study showed that approximately 43% of the losses were due to mechanical integrity failure, and this percentage was higher for losses caused by oil refinery. Of these mechanical failures, 70% were identified as a result of corrosion of process pipes, mainly due to internal corrosion. In cases where external corrosion was the cause, the cause was insulation corrosion.

Abstract Graphene is a two-dimensional (2D) planar sheet of carbon atoms in a hexagonal configuration where the atoms are sp2-bonded [33]. Graphene sheets are formed by placing carbon atoms together. In a graphene sheet, each tetravalent carbon atom is connected to three other carbon atoms with three covalent bonds. These three links are on the same plane and the angles between them are equal to 120 degrees. In 2004, a group of physicists at the University of Manchester, led by Andre Geim and Kostya Novoselev, used a simple and very different method to produce graphene, leading to a revolution in the field. Using 3D graphite, they produced a single sheet (a monolayer of atoms) of it through a micromechanical layer-by-layer method. This method led to the easy production of high-quality graphene crystals with dimensions of more than one hundred micrometers. Graphene, this two-dimensional nanomaterial (2D) is the newest member of the family of multidimensional carbon materials, which includes fullerenes as zero-dimensional nanomaterials (0D), single-walled carbon nanotubes (SWNT) as one-dimensional nanomaterials (1D), and graphite as a three-dimensional material (3D). Graphene is a two-dimensional allotrope of carbon (one atom thick) with a honeycomb structure. In terms of strength, this material is one of the strongest materials known so far. This composition is the basic constituent of carbon nanotubes and large fullerenes.

Abstract Corrosion is a critical concern in refinery operations, particularly within the Crude Vacuum Unit (CVU), where high temperatures, acidic compounds, and variable feedstock compositions create a highly aggressive environment. This study investigates the extent and nature of corrosion affecting major components of the CVU, including furnace tubes, transfer lines, flash zones, and overhead condensers. Using ultrasonic thickness gauging (UTG), process stream analysis, and historical inspection data, corrosion rates were calculated and correlated with metallurgical characteristics and process conditions. The findings reveal that the highest corrosion rates occurred in the transfer lines due to naphthenic acid corrosion (NAC), especially under conditions with Total Acid Number (TAN) exceeding 1.0 mg KOH/g and operating temperatures above 230°C. Sulfidation in furnace tubes and erosion-corrosion in overhead systems were also prominent. Material selection played a significant role in corrosion resistance, with carbon steel components showing higher degradation compared to low-alloy and stainless steel counterparts. The study emphasizes the importance of feed monitoring, material upgrading, and optimized wash water injection to mitigate damage. Recommendations include the use of corrosion-resistant alloys, installation of online monitoring systems, and improved process control strategies. This research provides valuable insights for maintenance planning and risk reduction in vacuum distillation operations, contributing to improved asset integrity and operational efficiency in refineries.

Abstract Today, the use of artificial intelligence in modeling chemical reactors has many advantages. In this project, the inputs and outputs of a nonlinear multi-input and multi-output system have been used to create an intelligent dynamic model. Therefore, the selection of artificial neural networks of the multilayer perceptron type is suitable for this purpose. Along with this type of modeling, it is necessary to use an appropriate method for predictive control of the mentioned model. Correctable regression models that use the rules for adjusting the weight matrices of the communication paths between the model neurons have been used in this project. These rules are used to train the system to control and achieve the desired output at later times. Learning in this system is also of the supervised learning type. In this way, the dynamic differential equation of the system is available. Therefore, the desired values for the target variable that the system should achieve are specified for future times and the output of the system using a predictive controller should always achieve these goals. The system studied in this project is a chemical reactor that is used to continuously mix reactant chemicals with defined concentrations and amounts and produce a product substance with a time-varying concentration, where the desired amount of this concentration at a specific time is the desired goal that the system should achieve. Also, instead of a real system, a software model is used to collect input and output data and finally, the results of this successful modeling prove the ability of intelligent modeling methods as presented in this research. Correctable regression models that use the rules for adjusting the weight matrices of the communication paths between model neurons are used in this project.