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(Volume: 2, Issue: 8)
Combined Cooling, Heating and Power from Geothermal Trigeneration Systems
Power generation, either from renewable or non-renewable resources, produce surplus heat as waste. Can this waste heat be harnessed and processed to provide additional power generation or to find usage in any other applications? Obviously yes. There are systems to provide power generation, heating loads and cooling loads, all the three in one shot by maximally utilizing a single energy resource of power and the name given to them are the “Trigeneration systems” or the “Combined Cooling, Heating and Power (CCHP) systems”. These systems usually have a power generation unit to generate electricity. The excess heat produced is then passed to an Exhaust Gas Heat Exchanger, Heat Recovery Steam Boiler and Absorption Chiller to produce hot water, steam and chilled water, respectively, for use in commercial or industrial heating and cooling applications. Moreover, these systems can also enable additional power generation from the obtained hot water or steam. Hence, high energy efficiency and reliability with very-limited exergy loss can be acquired with these systems. Nowadays, power generation from geothermal resources is seeking greater attention for its sustainable energy supply with low greenhouse gas emissions and reduced installation or operational costs. Heat in the earth’s inner core is never going to dwindle and the regions with geothermal reservoirs can definitely rely on it for their major source of power. Knowing the advantages of geothermal energy and to use it maximally for electricity generation, several researchers have investigated and proposed different trigeneration systems with geothermal energy source for various applications. Jialin Xu, Zhanguo Su, Junyan Meng, Yuzhong Yao, Mohammad Shahab Vafadaran and Ali Kiani Salavat, a team of five members from Iran, China and Thailand, have proposed one such geothermal trigeneration system, considering various thermodynamic, exergoeconomic and exergoenvironmental factors. The researchers’ interest was to provide an independent supply of power to varied heating and cooling equipment in a sport arena with numerous occupants involved in indoor sports activities. In their research in Process Safety and Environmental Protection, Elsevier, Vol. 177, the researchers have employed a Single-Flash Geothermal (SFG) system for power generation. Subsequent to power generation, the available useful energy left behind as hot air or steam was then harnessed and passed to three units: (i) Double-Effect Absorption Chiller (DEAC), (ii) Kalina Cycle (KC) and (iii) Organic Rankine Cycle (ORC). The former unit rendered chilled water, while the latter two units generated additional power using dissimilar working fluids. The researchers have optimally controlled the pressure and temperature of various units using multi-objective genetic algorithm, producing a net power output of 107.3 kW with 40.09% exergetic efficiency. Future researchers can come up with similar, but new and optimal trigeneration or even polygeneration systems of power production, so as to meet the growing electricity demand with zero energy wastage and greenhouse gas emissions. Image courtesy: www.freepik.com
Machine Learning with Gene Expression Microarray Data Aids CNS Cancer Diagnosis
Cancer, be it benign or malignant, is one of the fatal diseases that the people of the world is witnessing nowadays. The Central Nervous System (CNS) cancer, affecting the regions of the brain and the spinal cord, is a cancer kind that are commonly called brain or spinal cord tumours, rather than cancers. Is it not shocking that the American Cancer Society declares that about 18990 people out of 24,810 people, who are being expected to be diagnosed with malignant CNS cancer in 2023, is expected to fall under the hands of death? This figure actually increases as the statistics included only the malignant type and not the benign type. Hence, earlier diagnosis of this disease becomes more vital to know the severity of the disease, to allow treatment planning and to cut down the morbidity rates. In recent times, approaches based on Artificial Intelligence (AI) and Machine Learning (ML) have gained interest for disease diagnosis. The ability to handle large number of features to accurately diagnose the disease across different patients with special attention to each one of them in a non- invasive manner might be the cited as the reason. Additionally, disease diagnosis using gene expression microarray data have become much popular because it allows numerous genes of numerous gene expression patterns to be examined simultaneously. Deepak Painuli, Suyash Bhardwaj and Utku Kose, have compared twelve different ML-based approaches to find the best approach for CNS cancer detection, employing the gene expression microarray data from the Kent-Ridge Biomedical Data Repository. In their article in the European Chemical Bulletin, the researchers have identified that only the Logistic Regression- based ML model imparted 99.6% classification accuracy. However, the researchers state that the data availability and the considered sample size was small because of the privacy concern of the CNS cancer patients. So, upcoming researchers should find more generalized approaches with enhanced data collection methods to prevent the cancer-affected population from losing their life. Image courtesy: www.freepik.com
Geocell Mattress Reinforcement for Bottom Ash Improves Soil Stability
Soil stabilization and erosion control needs special attention, prior to land reclamation and laying roads, constructing buildings, embankments or other infrastructures. The geocell mattress are the most commonly used geosynthetic materials to aid this purpose, as they impart improved ground stabilization with extensive load bearing capabilities. The reason is that they have flexible and durable honeycomb- like cell structures made of High-Density Polyethylene (HDPE) or other appropriate materials to hold soil or other filler materials like, gravel or concrete, when an extensive load is applied. In recent years, waste recycling for safer waste disposal and sustainable development has gained prior importance. So, is it possible to construct geocell mattress from waste to maintain sustainability? Obviously, yes. Researchers have found that the bottom ash, which is the coarser by-product obtained after the combustion of coal, wood or other solid fuels, can be used as a filler material in geocell mattress because of their excellent load distribution ability and less environmentally-harmful nature. Further, there are also ongoing researches to process and use plastic bottles made of Polyethylene Terephthalate (PET) as the filler material in geocell mattress or to make the geocell itself. Hence the bottom ash produced from fuel combustion and the massively utilized PET bottles, which are nothing but the waste can be used constructively in an eco-friendly manner. However, the granular nature of bottom ash can cause settlement issues and produce uneven surfaces with lesser load-bearing capacity. Hence three researchers, namely Sufyan Ghani, Sunita Kumari and Anil Kumar Choudhary, have investigated whether the PET bottles as geocell mattress can increase the bearing capacity of bottom-ash soil to support loads. Utilizing bottom ash from the Usha Martin Steel Plant in Jamshedpur, Jharkhand, India, the researchers have affirmed in their article in the Iranian Journal of Science and Technology, Transactions of Civil Engineering, Springer, that Artificial Neural Networks- based machine learning models could accurately predict the settlement of bottom ash reinforced with PET geocell mattresses. Since urbanization is rapidly take place, land use associated with it is also rapidly increasing. Hence, research on soil upgradation using geosynthetics made of recycled waste has a great scope in the future.
Image Courtesy: www.vecteezy.com
Cloud Computing- Assisted Power Optimization and Battery Management Systems in Hybrid Renewable Energy Systems
Integrating hybrid renewable energy systems to the traditional or smart grid power systems have become utmost necessary to meet the growing demand for electricity and increased emphasis on decentralized and distributed energy generation. It is quite usual that the renewable energy systems do not fully operate in a day to generate power, due to changing weather or climatic conditions. Hence, energy storage in batteries and their management to charge or discharge power at varying load demands has a significant effect on power efficiency. In recent years, Cloud Computing- based Power Optimization Techniques (CC-POT) have gained greater research interest, since it can provide real-time monitoring of various on-site and off-site units in the power system. Additionally, it allows to predict future energy demands, provide load balancing, optimize resource utilization with improved scalability and flexibility, enhances power efficiency and renders continuous power supply without outages or fluctuations. Various researchers have shown interests on optimizing power and extending battery life in cloud- based power systems, being integrated with hybrid renewable energy systems, and their aim was to render a more reliable, cleaner and sustainable energy even to remote or off-grid areas. Ahmed Hadi Ali AL-Jumaili, Ravie Chandren Muniyandi, Mohammad Kamrul Hasan, Mandeep Jit Singh, Johnny Koh Siaw Paw and Mohammad Amir in vol. 10, Energy Reports, Elsevier, have discussed about the various advancements in intelligent cloud computing for optimizing and managing battery in hybrid renewable energy systems. The researchers have aimed to accomplish a cloud framework that can provide Battery Charging as a Service (BCaaS), implying that the battery charging is done without power wasting and carbon footprints for extended battery life and resource utilization. In addition, the researchers also state that an optimal Battery Management System (BMS) must optimize battery modeling, battery cell balancing, the control of State of Charge (SoC)/ Depth of Discharge (DoD), power consumption, battery life and thermal management. So, the upcoming researchers have a great way ahead in cloud- assisted power system optimization and battery management, eventually enabling the power to be harnessed for meeting the future energy demands. Image Courtesy: www.vecteezy.com
Technological Perspectives for Fighting Cancer using Phytochemicals
Have you ever heard of the term ‘phytochemicals? They are a wide range of compounds produced by the plants to help them grow and develop, in addition to preventing them from pests’ attack, unbearable temperature or harmful radiations. But what makes them special is their antioxidant, anti-inflammatory, anti-mutagenic and antiproliferative nature in treating the deadliest disease- ‘The Cancer’. Homa Fatma and Hifzur Siddique, the researchers of Molecular Cancer Genetics & Translational Research Lab, Aligarh Muslim University, Uttar Pradesh, India have discussed about various researches and patents on few phytochemicals, like flavonoids, tannins, polyphenols, triterpenes, coumarins, saponins and terpenoids, which can fight against cancer. As per the researchers’ article in vol. 18(4), Recent Patents on Anti-Cancer Drug Discovery, a combinatorial therapy involving a traditional therapy and the phytochemical utilization can apprehend the growth of cancerous cells. Additionally, the researchers have also pointed that the future research can be centered around the selection of optimum dosage levels of phytochemicals in humans and their bioavailability. So, what are the various research opportunities associated with cancer treatment using phytochemicals? Can Machine Learning (ML) and Artificial Intelligence (AI) approaches be employed at any stage to support this notion? Obviously, yes. The ML or AI approaches can be employed for the following: (i) To identify the phytochemicals from various plant parts using microscopy images or spectroscopy data, (ii) To predict whether the identified phytochemical is toxic or not (iii) To predict the bioactivity of the phytochemicals as whether it suppresses the carcinogenic cell growth or a specific enzyme secretion, (iv) To predict the dosage levels for intake in humans (v) To predict the proportion of mixing the scarcely-available phytochemical with other compounds and (vi) To predict an analogue of phytochemical from its naturally-occurring constituent compounds to ensure bioavailability. Hence, the researchers aspiring to find methods for treating cancer have boundless things to explore in the context of ‘phytochemicals to fight against cancer’. Image Courtesy: www.freepik.com
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