AAEES Student Branch, University of Dhaka

22/04/2024

Earth Day

16/03/2024

08/07/2023

08/07/2023

08/07/2023

https://www.thedailystar.net/business/economy/news/bscic-plans-tk-800cr-project-make-tannery-cetp-fully-functional-3362991?fbclid=IwAR2IQERUcR8p7SI8KgKjHaDJwGplOZTV5EvBJXKz0lqVs55GbRcf5A45gf0

The Bangladesh Small and Cottage Industries Corporation (BSCIC) has sent a proposal to the industries ministry for taking a project that will make the Central Effluent Treatment Plant (CETP) at the Savar Tannery Industrial Estate (STIE) fully functional.

18/05/2023

The water treatment process in a nuclear power plant is crucial to ensure the safe and efficient operation of the plant. Here are the general steps involved in water treatment for a nuclear power plant:

Intake: The process begins with the intake of water from a nearby source, such as a river, lake, or ocean. The water is usually drawn through screens or filters to remove large debris and particles.

Pre-Treatment: After intake, the water undergoes pre-treatment to remove impurities and contaminants that could potentially damage the equipment or affect the performance of the nuclear reactor. Pre-treatment typically involves processes like sedimentation, coagulation, flocculation, and filtration to remove suspended solids, organic matter, and other impurities.

Ion Exchange: Nuclear power plants often employ ion exchange processes to remove dissolved impurities from the water. Ion exchange resins are used to exchange undesirable ions, such as calcium, magnesium, and trace metals, with harmless ions like sodium. This process helps prevent scaling and corrosion in the plant's components.

Reverse Osmosis: Reverse osmosis (RO) is another common method used in water treatment for nuclear power plants. RO involves passing the water through a semi-permeable membrane under high pressure, effectively removing dissolved salts, minerals, and other impurities. RO is particularly useful for desalinating seawater or brackish water sources.

Deionization: Deionization, also known as demineralization, is a process that further removes dissolved ions from the water. This step helps achieve high-purity water required for various cooling and steam generation processes in the power plant. Deionization can be accomplished using mixed bed ion exchange resins or electro-deionization techniques.

Chemical Treatment: Chemical treatment is performed to control the pH levels and prevent corrosion in the nuclear power plant's systems. Various chemicals, such as corrosion inhibitors, pH adjusters, and biocides, may be added to the water to maintain optimal conditions and inhibit the growth of microorganisms.

Cooling and Condensation: The treated water is used for cooling purposes in the power plant. It absorbs heat generated by the reactor and is circulated through cooling towers or heat exchangers to remove excess heat. The water then goes through a condensation process to convert steam back into liquid form, which can be reused in the system.

Monitoring and Quality Control: Throughout the water treatment process, continuous monitoring and quality control measures are implemented to ensure that the treated water meets the required standards and regulatory guidelines. Parameters such as pH, conductivity, turbidity, and microbiological content are monitored to maintain water quality and plant safety.

18/05/2023

Nanotechnology has the potential to enhance various aspects of wastewater treatment, including in nuclear power plants. While specific applications of nanotechnology in nuclear power plant wastewater treatment may still be under development, here are some potential steps that could be involved:

Pre-Treatment: Similar to conventional wastewater treatment, the process can start with pre-treatment steps to remove larger particles and debris from the wastewater. This can be done through physical processes like screening or sedimentation.

Nanofiltration/Reverse Osmosis: Nanofiltration and reverse osmosis are membrane-based processes that use nanoscale pores to separate dissolved ions, particles, and contaminants from water. These technologies can be employed to effectively remove radioactive species, heavy metals, and other dissolved impurities from the wastewater.

Adsorption: Nanomaterials, such as activated carbon nanoparticles or nanoscale zeolites, can be utilized as highly efficient adsorbents to remove specific contaminants from the wastewater. They can selectively adsorb radioactive elements, heavy metals, or organic pollutants, enhancing the overall treatment efficiency.

Photocatalysis: Nanoparticles with photocatalytic properties, such as titanium dioxide (TiO2) nanoparticles, can be employed to degrade organic pollutants in the wastewater. These nanoparticles, when activated by ultraviolet (UV) light, produce reactive oxygen species that can break down and mineralize organic compounds into harmless byproducts.

Advanced Oxidation Processes (AOPs): AOPs involve the use of nanoscale catalysts, such as nanoparticles of iron or manganese oxides, to facilitate the degradation of recalcitrant organic compounds and the destruction of toxic substances. These catalysts can enhance the efficiency of chemical oxidation processes by promoting the generation of highly reactive hydroxyl radicals.

Filtration and Separation: Nanotechnology-based filtration techniques can be employed to further remove suspended solids, nanoparticles, and fine colloidal particles from the wastewater. Nanofiber filters or ceramic membranes with nanoscale pores can effectively capture and retain these small particles, producing a cleaner effluent.

Monitoring and Control: Nanotechnology can also contribute to monitoring and control aspects of wastewater treatment in nuclear power plants. Nanosensors can be used to detect and quantify the presence of specific contaminants or radioactive species in real-time, enabling prompt actions and ensuring compliance with regulatory limits.

18/05/2023

Combining nanotechnology with machine learning techniques can offer advanced wastewater treatment solutions for nuclear power plants. Here are the potential steps involved in wastewater treatment using nanotechnology and machine learning:

Data Collection and Monitoring: Machine learning algorithms can be utilized to collect and analyze data from various sensors and monitoring devices within the wastewater treatment system. This includes data on water quality parameters, contaminant levels, flow rates, and other relevant variables.

Real-Time Optimization: Machine learning algorithms can continuously analyze the collected data and make real-time decisions for optimizing the treatment process. By considering multiple variables simultaneously, these algorithms can dynamically adjust the operation of nanotechnology-based treatment units for optimal performance.

Nanomaterial Design and Synthesis: Machine learning techniques can be employed to design and optimize nanomaterials for enhanced wastewater treatment efficiency. By analyzing the properties and behavior of different nanomaterials, machine learning algorithms can identify optimal combinations and compositions that exhibit superior adsorption, catalytic, or membrane separation properties.

Nanoparticle-Assisted Treatment: Nanomaterials, such as nanoparticles, nanofibers, or nanocomposites, can be incorporated into the wastewater treatment process to improve contaminant removal. For example, machine learning algorithms can help determine the optimal dosage of nanoparticles for adsorption, catalytic degradation, or membrane-based separation processes.

Smart Control Systems: Machine learning algorithms can be integrated into the control systems of wastewater treatment plants to optimize the operation of nanotechnology-based units. By learning from historical data and real-time inputs, these algorithms can adjust process parameters, such as flow rates, dosages, and treatment sequences, to achieve the desired treatment goals efficiently.

Predictive Maintenance: Machine learning algorithms can analyze sensor data and historical records to predict the maintenance needs of nanotechnology-based treatment units. By identifying patterns and correlations, these algorithms can anticipate equipment failures or degradation, enabling proactive maintenance and minimizing downtime.

Decision Support Systems: Machine learning algorithms can assist operators and engineers in making informed decisions regarding treatment processes and troubleshooting. By analyzing historical data, operational parameters, and treatment outcomes, these algorithms can provide recommendations and insights for optimizing the wastewater treatment system's performance.

Adaptive Learning and Optimization: Machine learning algorithms can continuously learn from the treatment system's performance data and adjust their models and predictions accordingly. This adaptive learning capability allows for ongoing optimization of the wastewater treatment process, considering changing influent characteristics, system dynamics, and performance requirements.

08/04/2023

https://indd.adobe.com/view/3f8a211a-2ed6-4a46-b658-f61b4b6bfac4

Winter 2023 Environmental Engineer and Scientist

25/03/2023

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9779100/

For the first time in the territory of the Russian Far East, a study related to the establishment of correlations between air quality and public health in Ulan-Ude (Buryatia, Russia) was carried out. This study is based on the analysis of official medical ...

25/03/2023

https://www.nature.com/articles/s41370-022-00423-y

Climate mitigation policies that focus on the transportation sector yield near-term health co-benefits that could motivate policy action. We quantified CO2 emission reductions as well as the air pollution and health benefits of urban transportation policies promoting electric vehicles (EV) and walki...