Modern power systems rely on advanced technologies and communication networks that enable efficient monitoring, control, and operation of physical equipment. These tools offer diverse services in terms of functionality and enhanced reliability; however, their increasing use has led to a rise in vulnerabilities and cyberattacks. Such vulnerabilities have serious consequences, including blackouts and detrimental effects on electrical infrastructure. Therefore, developing effective models for identifying and classifying adverse events in power systems for the prompt issuance of control commands is of great importance. In this context, cyberattacks on SCADA systems, which are widely used for monitoring large production systems and modern power networks, have become a significant challenge. These systems are particularly vulnerable to various cyberattacks due to their unique features, including remote communications and management of critical infrastructure. Detecting these attacks using traditional Intrusion Detection Systems (IDS) is challenging due to the complexity and diversity of threats, highlighting the need for innovative and effective methods for accurate identification of these attacks. In this regard, a two-stage feature selection method utilizing Principal Component Analysis (PCA) and the War Strategy Optimization (WSO) algorithm, along with a Feedforward Neural Network (FFNN), has been proposed. In this method, the number of layers and the number of neurons in each layer of the FFNN are optimally determined by the WSO algorithm. This approach is designed for the precise identification of malicious attacks. The performance of the proposed method has been evaluated using laboratory SCADA system data, and the results demonstrate satisfactory effectiveness in detecting cyberattacks. This research can significantly enhance the resilience and safety of SCADA systems against cyber threats and serve as an effective solution for managing and mitigating risks arising from cyberattacks on critical infrastructure.

  The construction industry is one of the most important industries in driving the economic cycle of countries. The significance of this industry is especially notable in developing countries where there is a greater need for new infrastructure development. On the other hand, the use of modern building methods and advanced technologies to improve quality, reduce time and costs, and increase productivity has always been a focus for stakeholders in this field. One of the most important mechanisms that have emerged in recent years is "Building Information Modeling/Management," or BIM for short, which specifically focuses on improving the construction industry in its initial stages. Building Information Modeling (BIM) reduces common engineering errors by creating a consolidated information structure through digital tools and providing a unified view of all information and managerial and engineering decisions. This process is currently being researched and used by leading companies, research groups, and governments worldwide. Implementing this process in our country requires relevant methods, guidelines, and infrastructure. Unfortunately, like in other countries, there are challenges to overcome. This thesis aims to facilitate the implementation of the Building Information Modeling (BIM) process by creating an EIR (Employer’s Information Requirements) worksheet as the first step in implementing the BIM protocol. It seeks to provide a clear understanding of BIM and its practical applications to senior managers and decision-makers. By reviewing library resources and designing a case study aligned with BIM process capabilities and compiling information, this thesis results in a proposed "Employer's Information Requirements" worksheet and provides solutions for completing such worksheets in accordance with higher-level documents.

The increase of population has led to increase the demand for housing, which in contrast to the insufficient and inappropriate supply of housing And due to the lack of economic power and the high share of housing costs in the household budget, the low-income sections of the society. Having a suitable house for the unemployed and with various conditions such as mortgage, rent and marginalization in the units They live in unfavorable housing. Designing a suitable housing that fits the needs of the target community and paying attention to the cultural conditions that govern those communities is one thing It is one of the solutions to meet the needs of low-income groups for housing. Therefore, in this thesis, try to It is that by determining the characteristics of the ideal housing and examining the basic human needs, the ideal residential complex designed for the low-income text-align:justify;text-justify:inter-ideograph;direction:ltr;unicode-bidi: embed;mso-line-break-override:none;word-break:normal;punctuation-wrap:hanging">The society's income leads to solving the problem of housing poverty, security and public welfare and according to quantitative aspects And the quality of housing will increase the level of satisfaction of the residents. Considering that it is the core of the city of Kermanshah. It is a historical context, the aforementioned design idea is also an answer to the problem of low-income housingIt is on a local scale, it can help the issues and problems in the historical context of Kermanshah city is also In recent years, the concept of urban improvement has become one of the most important methods of organizing dilapidated urban tissues And areas with informal texture have become. These concepts are also considered in our country. Textures Worn out and informal has become one of the challenges of citizenship management in the past decades. From Also, the comparison of the potential of two worn-out and informal contexts for improvement is considered one of the important issues which has been less investigated. The present study aims to compare the capacity of urban regeneration in wealthy neighborhoods. The worn-out and informal urban texture has been done in Kermanshah city. This research is applied and descriptive. It is analytical. Its target population includes low and middle income groups who can afford quality housing. They are not desirable in suitable neighborhoods in the center of the city, as the residential context of Modares street in Kermanshah. It is worn out and selected for review and design. The results of this thesis show From the point of view of the residents of the target neighborhoods, the improvement of social conditions is the first indicator with potential in urban improvement. Economic prosperity is the second indicator with improvement capacity, physical improvement is the third indicator with potential Improving the environmental status of the fourth indicator has potential in urban improvement. Improved capacity A city in the micro-scales of socio-cultural conditions, economic prosperity, improvement of the environmental situation and so on Improving physical quality in worn tissue is high and high. Urban improvement capacity in this area Scales are also average in the informal context. In total indicators, the capacity of urban improvement in Worn-out texture is more than informal texture that should be considered by urban improvement managers.   

  The increasing construction of high-rise structures in cities and the placement of buildings on soils with different layers show the need for engineers to pay attention to the effect of different conditions of soil layers on the seismic response of structures. In this study, after validating the 3D numerical model using the previous shaking table test, the effect of soil layering on the seismic response of resistant concrete buildings has been investigated by considering the soil-structure interaction (SSI). Using Abaqus finite element software, a set of numerical modeling for a 15-story building placed on layered soil with different values of shear wave velocity has been simulated. Nonlinear dynamic analysis under seismic motion has been performed in a direct way and the results have been compared and discussed in terms of maximum lateral displacement, shear force, maximum relative displacement of floors and acceleration response spectrum. The results showed that the deeper the soil layer with the lower shear wave speed and the closer it is to the ground surface, the higher the values of lateral displacement and shear force, the relative displacement of layers and the acceleration response spectrum. Also, according to the results, soil layering has a major contribution to the seismic response of buildings by considering SSI, and considering SSI ensures the safe and economical design of structures. Keyword: soil-structure interaction, numerical modeling, soft soil, abacus, relative displacement, floor shear

  In today's urban society, where the stress and consequences of machine life have created the ground for all kinds of physical and mental diseases, spa as a reliable and basic solution to improve human mental and physical health, to avoid today's stressful life and to prevent diseases. It is spiritual. On the other hand, the proper and special use of the healing garden in combination with the spa can create a favorable and relaxing atmosphere to improve the health of the human body and soul and create a healing place. This issue has not been given much attention in our country and the result is an increase in tension, stress and psychological crises. This is despite the fact that most cities in Iran, including Kermanshah, have a high potential for setting up spa and healing garden complexes.Spas have different types according to the services they provide, however, the general goal of all of them is to improve the level of health and well-being of people and maintain, manage and improve health and well-being. The spa that is considered in this thesis is a health spa. Thus, the main goal of this thesis is to provide a framework for the design of the health spa complex in Kermanshah using the component of healing gardens. Reviewing the literature done around achieving the goal of the thesis has led to the formation of the main research question.In this way: What are the conceptual and practical components and principles of garden healing in the architecture of the spa complex? Also, how can these components be properly used in spa design?The approach adopted to answer the research question is qualitative, and the required information was collected using library sources and documents and scientific articles, then the components and healing solutions of the garden in the spa were extracted and deduced with the descriptive-analytical method. Is. The findings of the research show that the four components of stimulation of the five human senses - the feeling of comfort, ease and relaxation - the proper connection and access of the spa to the garden - the feeling of closeness, confidence and control of the environment are the effective components of the healing of the garden in the spa. In the following, each of the components is reviewed and the solutions and design principles related to it are presented.Also, considering the importance of the selected site in the design of the spa and healing garden, these 4 components have been investigated and evaluated in the site using the SWOT table, and after ensuring the suitability of the site, appropriate design planning has been presented.Finally, it has been tried to present the main results of the research by providing a suitable model in the design of this collection. The results of this research show that the design of the spa complex by using the healing garden, by creating a proper relationship between architecture and nature and establishing a balance, creates a healing environment that reduces stress, provides the ground for mental regeneration and causes Promoting the culture of health, wellness and healthy lifestyle

  With the rapid growth of the economy and the expansion of the stock market, analyzing and forecasting the stock price and comparing various price forecasting methods, and analyzing the trend of the stock market are more necessary and at the same time popular.The stock market is difficult to predict due to its volatile nature.There are no rules for predicting what will happen to stocks in the future.Accurate forecasting is a big challenge because market trends are always changing depending on many factors. In this research, the goal is to analyze the price and trend of the total index and stocks using machine learning techniques.This work includes two approaches. In the first approach, using the historical data of oil, gold, dollar, some other foreign indices, shares of some large stock exchange companies inside Iran, stock indices, and technical indices extracted from them between 11/13/2012 and 05/21/ 2022, was shown that with the help of artificial intelligence and machine learning algorithm (MLP), it is possible to find the factors and indicators that affect the total index of the Tehran stock market and try to better predict prices with the help of them and machine learning algorithms.The results indicate that the LSTM network with two recurrent layers and the optimal time step is a suitable and time-consuming but high-accuracy network for price and time series forecasting, which has the best results with minimal error compared to other machine learning methods such as nearest neighbor. followedIn the second approach, using some machine learning algorithms and technical indicators and past price information of a specific stock (steel from 03/11/2007 to 08/30/2022), the goal was to analyze the stock trend and check the buying and selling signals that With the help of the Bollinger Band indicator and a buy and sell risk factor, you can find the right time signals for the predicted data.

  The purpose of thisthesis is to investigate different deep learning techniques that can be used toimplement a system that learns how to detect breast cancer cases inmammography. Today, breast cancer has become one of the deadliest diseases.Mammography is the gold standard for detecting early signs of breast cancer,which can help treat the disease in its early stages. However, mammographymisdiagnosis is common and may harm patients through unnecessary treatments andprocedures (or lack of treatment). Therefore, systems that can learn to detectbreast cancer on their own can help reduce the number of misinterpretations andmissed cases.Convolutional NeuralNetworks (C  ) are used as part of the deep learning process, initially ontheir own. To analyze the effects on performance and efficiency, various deeplearning techniques such as different architectures (VGG16), dropout, dataaugmentation, changing network layers, fine-tuning, etc. are used.Finally, the accuracy of88.69% in the CBIS-DDSM dataset with the pre-trained model with VGG16architecture in two-class classification for the detection of mass andcalcification, as well as the accuracy of 61.31% in the four-classclassification for the simultaneous detection of mass and Calcification andtheir benign and malignant nature are obtained in mammography images. Othertested techniques such as data augmentation, dropout, and fine-tuning alsoincrease accuracy. Finally, these results have been compared with otherarticles using the CBIS-DDSM dataset.

   Currently, fMRIis the most widely used brain function technique. fMRI provides an oppurtunity to observe the neural activity in the brain. In fMRI tests, a blood oxygen level dependent (BOLD) signal is measured. Dynamic causal modeling is a Bayesian framework for inferring latent neural states from measured brain activity. DCM is increasingly used in the analysis of a wide range of neuro imaging and electrophysiological data. One of the methods of functinal magnetic resonance imaging is functional magnetic resonance imaging at rest. The idea that cerebral blood flow can reflect neural activity is the basis of all imaging techniques today. rs-fMRI imaging is used to describe areas of the brain that are correlated with time signals. Spectral Dynamic causal modelling (sp DCM) is introduced to estimate the intrinsic effective connectivity of rs-fMRI data. spDCM estimate the effective connectivity that results in a functional connectivity. spDCM reliably estimates the intrinsic efffective connectivity in the absence of an external stimulus. Spectral DCM models a range of endogenous activities that reproduce the observed functional connectivity fMRI at rest. The aim of this thesis is to investigate both DCM methods for inferring latent neural states from measured brain activity and spDCM methods for estimating the intrinsic effective connectivity of rs-fMRI data. Currently, fMRIis the most widely used brain function technique. fMRI provides an oppurtunity to observe the neural activity in the brain. In fMRI tests, a blood oxygen level dependent (BOLD) signal is measured. Dynamic causal modeling is a Bayesian framework for inferring latent neural states from measured brain activity. DCM is increasingly used in the analysis of a wide range of neuro imaging and electrophysiological data. One of the methods of functinal magnetic resonance imaging is functional magnetic resonance imaging at rest. The idea that cerebral blood flow can reflect neural activity is the basis of all imaging techniques today. rs-fMRI imaging is used to describe areas of the brain that are correlated with time signals. Spectral Dynamic causal modelling (sp DCM) is introduced to estimate the intrinsic effective connectivity of rs-fMRI data. spDCM estimate the effective connectivity that results in a functional connectivity. spDCM reliably estimates the intrinsic efffective connectivity in the absence of an external stimulus. Spectral DCM models a range of endogenous activities that reproduce the observed functional connectivity fMRI at rest. The aim of this thesis is to investigate both DCM methods for inferring latent neural states from measured brain activity and spDCM methods for estimating the intrinsic effective connectivity of rs-fMRI data.

   Nowadays power quality is one of the most important index in power systems along with other indices such as reliability, stability and security and can be affected by steady state current or transient current including short-circuits. Directional over current relays are used as the primary protection of meshed distribution and multi-sourced sub transmission systems. By using a good protection scheme, in case of fault current occurance, only the least possible portion is removed. DOCRs have mainly two settings i.e Time multiplier setting (TMS) and plug setting (PS). Proper coordinated relayes are required to clear the fault as soon aspossible to avoid further damages to the transmission system. This problem of setting proper TMS and PS of DOCRs in multi-sourced meshed systems has become very complex. CTs provide DOCR   the required current to trigger. Numerous researches and studies have been done over solving and optimizing the problem of setting proper settings among primary and back up relays but none considered CT composite error. Therfore we are about to solve the complicated phenomena of setting and optimizing TMS and PS considering the composite error of CTs. To achieve this goal we have used metaheuristic optimizing algorithms such as PSO,DE and GA combined with DE.

Advances in Blockchain and distributed ledger technologies are driving the rise of incentivized social media platforms over Blockchains. Blockchain-based online social media is decentralized social media that uses blockchain technology to reward users' social activities and store information. In order to protect the privacy of users and expose fake news.    In this study, presents an empirical analysis of Steemit, a key representative of these emerging platforms, to understand and evaluate the actual level of decentralization in these modern social media platforms. Similar to Bitcoin, Steemit is operated by a decentralized community, where 21 members are periodically elected to cooperatively operate the platform through the Delegated Proof-of-Stake (DPoS) consensus protocol.    Our study performed on 539 million operations performed by 1.12 million Steemit users during the period 2016/03 to 2018/08 reveals that the actual level of decentralization in Steemit is far lower than the ideal level, indicating that the DPoS consensus protocol may not be a desirable approach for establishing a highly decentralized social media platform. For this reason, in this dissertation, we tried to provide a solution to the problem of decentralization of the consensus algorithm used in Steemit social media. Our solution to this problem is to replace its consensus algorithm with a more advanced consensus algorithm called the Algorand, which can form a committee without elections involving user interaction. Algorand is a new cryptography that proposes a new Byzantine agreement algorithm that allows choices to be made by randomly validated cryptographic functions rather than by users.    Using the simulator design as well as the API published by Algorand's team, we explored its three main aspects of decentralization, high scalability and security, and show that Algrand can be a good alternative to the DPOS algorithm. Be in Steemit.      

Earthquakes and that associated effects occur every year in different areas of the world and a lot of damage to all sectors and national infrastructure, including various industries and facilities that this issue leads to the loss of Financial and spiritual valuable capital at the macro level. One of the most important parts that affected by earthquakes has high vulnerability is the power distribution network. The electricity distribution network is one of the most important and basic industries in people's lives, and this issue important has more manifestation, especially when the earthquake occurs. The purpose of this study is the assessment of the vulnerability of electric distribution network againstsite frequency resonance in Kangavar city, which this work is based on frequency microzonation done in Kangavar city in previous research and modeling components and electricity distribution equipment used in Kangavar city with Abaqus software and analyze and drawing the seismic risk maps of the electricity distribution network. For this research, the modeled components and equipment include 9m distribution reinforced concrete poles with a nominal strength of 200 and 400 kg force and 12m with a nominal strength of 200 and 400 force which 9m pole with a nominal strength of 200 kg force according to the role its passage and branch is without transformer equipment and 9m pole with anominal strength of 400 kg force is modeled in the forms of single pole without transformer equipment, single pole with 200 kVA transformer, double pole without transformer and double pole with 250 kVA transformer and regarding 12mpoles with nominal strength 200 kg force, the modeling is in the forms of single-pole without transformer equipment, single pole with 50 kVA transformer, double pole without transformer and double pole with 50 kVA transformer, and 12m pole with nominal strength of 400 kg force is in the forms of single-pole without transformer equipment, double pole without transformer and double pole with 350 kVA transformer. The results separately include the first to third deformation modes of each component and accompanying equipment. The output of thesemodes is in the form of frequency, which is extracted from Abacus software as the normal frequency of equipment. The frequency in a single 9m distribution reinforced concrete pole with a nominal strength of 200 kg force without accompanying equipment in all three movement modes is in the range of 1.4 - 7.2 Hz, in a single 9m distribution reinforced concrete pole with a nominal strength of 400 kg force without accompanying equipment is in the range of 1.5-9.2 Hz, in a single 12m distribution reinforced concrete pole with a nominal strength of 200 kg force without accompanying equipment is in the range of 1.0– 5.0 Hz, and in a single 12m distribution reinforced concrete pole with anominal strength of 400 kg force without accompanying equipment is in the range of 1.2 – 6.8 Hz. These frequencies decrease with the increase of weight of components and accompanying equipment. By comparing these values of equipment frequency with the natural frequency of the ground that has been measured in previous research, the vulnerability is investigated. The result of the research is shown, in the form of tables and risk maps. The result of this study shows that the amount of risk of the main components and equipment of electricity distribution due to site frequency resonance in Kangavar city is different. The results indicate that the eastern and southern areas of Kangavarare more at risk

   Due to the increasing expansion of renewable energy sources in the world, the use of the concept of microgrid has been much considered by researchers. Microgrids are small distribution networks that can operate in both island and network-connected modes. Creating a suitable control platform for the best use of microgrids is of great importance. One of the problems regarding the use of renewable energy sources is the low level of inertia and the lack of inertia in these sources. Therefore, to overcome this problem, a concept called virtual synchronous generator has been introduced. Virtual synchronous generator using oscillation equation will help to create a virtual inertia to simulate behavior like real synchronous generator. In addition to this issue, another challenge that can be raised in this regard is the issue of voltage quality in microgrids. Different factors will cause unbalance.   In this Thesis we have tried to examine these problems. The possibility of switching on low voltage is one of the most basic needs of the network because during a voltage drop in the network, in fact, a series of incompatibilities will be formed between the active power output and the reactive power delivered to the network. By applying this scheme, which uses a proper power distribution between active and reactive capacities, it is possible to eliminate this voltage drop without any other faulty network specifications. In order to evaluate the correct operation of the network under voltage unbalance, the low voltage switching strategy (LVRT) and decouple dual synchronous reference frame (DDSRF) have been used. To prove the proposed method, a microgrid with two parallel distributed generation units is included in this work. Instead of the usual drop control that is often used in previous work, the concept of a virtual synchronous generator has been used alongside the LVRT strategy.

    Numerical Analysis of the influence of the aspect ratio and angle of tube in vacuum tube solar collectors with modified structure

  Iran has a large volume of bitumen mines, especially in Kermanshah province. The purpose of this study is to remove ash and sulfur pyrite from the bitumen by leaching with sulfuric acid (as pretreatment) followed by flotation. This process is performed to produce high-quality bitumen and use of more than 160 applications of this mineral in various industries. In order to determine the optimal conditions and evaluation of effective leaching parameters, 27 experiments were designed by using a central composite design by considering four factors including acid concentration, temperature, and time, and stirring rate at three levels as follows; atmospheric pressure, particle size of 200 mesh and solid- to -liquid ratio) S/L) of 0.2gml-1. The intervals considered for independent parameters were including acid concentration (10-30 v/v%), leaching temperature (30-90?), stirring rate (0-1000rpm), and leaching time (30-90 min). The obtained experimental data were analyzed by Analysis of Variance (ANOVA). In addition, the second-order regression model was estimated as the most suitable model with respect to the 99% confidence level for analyzing the responses. Based on statistical model, the maximum removal of ash and pyritic sulfur were obtained in acid concentration of 29.57 v/v %, temperature of 89.98°C, stirring speed of 977.10 rpm and the time 89.74min. Following confirmation tests, 63. 52% ash removal and 38.82% pyrite removal were obtained in these conditions. stirring speed and temperature were the most effective parameters for the ash removal process and removal of pyritic sulfur, respectively. All parameters had a positive effect on the removal process.  flotation test was subsequently carried out in two conditions, as follows: The first was conducted on primitive bitumen sample and the second state stage was performed on bitumen sample in optimal conditions (effect of leaching was considered as pretreatment). The flotation test was conducted under following operating conditio   the collector amount of 1kg / t bitumen, foaming amount of 50 g / tbitumen, pulp equal to 5% of solid, pH =7, the particle size of 200 mesh and the flotation time of 3min based on the ambient temperature and atmospheric pressure.   In the first state, 42.3% of ash and 48.1% of sulfur pyrite (33.6 % of total sulfur) were removed while 79.4% and 57.6% (42.15%) removal of ash and pyrite (removal of total sulfur) were achieved in the second state, respectively. With regards to previous papers and the correlation of experimental data, the removal of pyrite follows a second-order kinetic model. A second-order regression model is consistent with the experimental data, due to the high value of adjusted determination coefficient (0.9752) and its proximity to the determination coefficient (0.9828).

  Purification of Xanthan biopolymer for pharmaceutical use  Abstract  The use of biopolymers to achieve a biocompatible material with appropriate properties has attracted many researchers. Polysaccharides are one of the most important family of natural polymers, xanthan gum is a microorganism exopolysaccharide produced by Xanthanamus Campestris the bacterium. This heteropolysaccharide is composed of two molecules of glucose, mannose and a unit of glucuronic acid with subsequent pentasaccharide units. Xanthan gum has various industrial applications due to its solubility in cold and hot water and the production of high viscosity, even at low concentrations, compatibility with acids, bases and salts, resistance to ambient temperature and rheological pseudoplastic behavior.In this study, two factors were investigated: xanthan biopolymer purification was performed using three solvents: acetone, ethanol, isopropanol, and the degree of purity of the biopolymer, acetone, ethanol and isopropanol, respectively, had the greatest effect on the purification of the biopolymer, respectively. In another study, the effects of bio-polymer concentration, solvent type and nanoparticle were used to determine the sediment using a pilot design . The results show that nanoparticle, solvent and xanthan concentrations have the greatest effect on sedimentation rate, respectively.Keywords: Xanthomonas campestris, purified, Nanoparticles، organic solvents

  1022/5000The purpose of this research is to use a response procedure to optimize the biological production of xanthan gum using agricultural waste. In this study, Xantamonas   . PTCC 1473 was used to produce xanthan gum. For growth of cell mass, YMA and YMB [2] were used. In this study, different concentrations of date palm as carbon source, soybean meal as a source of nitrogen and potassium dihydrogen phosphate as a source of phosphorus were investigated for optimal production of xanthan gum. Palm sauce was studied at concentrations of 40, 50 and 60 g / l. So that the best concentration of carbon source was 40 gr / lit. The maximum amount of xanthan gum produced in this method was 6.76 gr / lit. At concentrations above the carbon source (50 and 60 grams per liter), due to the inhibitory effect of the substrate, the amount of xanthan gum production decreases. The optimum concentration of nitrogen and phosphorus sources in this experiment was obtained at 20 gr / lit and 15 gr / lit respectively for soybean meal and potassium dihydrogen phosphate respectively. Which reduces or decreases the concentration of nitrogen and phosphate sources.

Mercaptans, sulfides and disulfides are compounds that exist in fossil fuels, which creates disadvantages such as odor, corrosion and reduce the fuel heating value. In the present study, adsorptive Demercaptanization of condensate via modified activated carbon by concentrated nitric acid and then loaded with silver and copper metal ions and also, silver metal ions loaded copper-benzene three carboxylate (Cu-BTC) metal-organic framework were examined. Condensate with mercaptan content of 188 ppm was purchased from Fajr Jam Gas Refinery. The mercaptan content of samples was measured by titration method. After adsorption experiments, adsorption isotherms correlation parameters for Langmuir and Freundlich models and kinetic parameters for pseudo-first-order and pseudo-second-order kinetic models were studied. In this study, Cu-BTC adsorbent shows a good absorption performance in adsorption of mercaptan compounds from gas condensates. The maximum adsorption capacity for Ag loaded Cu-BTC and treated and then Ag and Cu loaded activated carbon were obtained 49.416 mg/g and 4.824 mg/g respectively. Also, the SEM, XRD, EDX and FT-IR tests were used to approve the structure of Cu-BTC MOF, metal loading confirmation, absorbent surface morphology, crystal structure of absorbent and the adsorbent structure stability.