Techno-economic optimization and Nox emission reduction through steam injection in gas turbine combustion chamber for waste heat recovery and water production.

Considering the persistent human need for electricity and fresh water, cogeneration systems based on the production of these two products have attracted the attention of researchers. This study investigates a cogeneration system of electricity and fresh water based on gas turbine (GT) as the prime mover. The wasted energy of the GT exhaust gases is absorbed by a heat recovery steam generator (HRSG) and supplies the superheat steam required by the steam turbine (ST). In order to produce fresh water, a multi-effect desalination (MED) system is applied. The motive steam required is provided by extracting steam from the ST. In order to reduce the environmental pollution of this cogeneration system, the steam injection method is proposed in the GT's combustion chamber (CC). This system is optimized by a multi-objective optimization tool based on the Genetic Algorithm (GA). The design variables include pressure ratio of compressor (CPR), inlet temperature of gas turbine (TIT), steam injection mass flow rate in the CC, HRSG operating pressure, HRSG evaporator pinch point temperature difference (PPTD), steam pressure of the MED ejector, ejector motive steam flow rate, number of MED effects, and return effect. The goals are to minimize the total cost rate (TCR), which includes the cost of initial investment and maintenance of the system, the cost of consumed fuel, and the cost of disposing of CO and NO pollutants, as well as maximizing the exergy efficiency. In the end, it is observed that the steam injection in the CC leads to the reduction of the mentioned pollutant index, and it is proposed as a suitable solution to reduce the pollution of the proposed cogeneration system.

Optimization of water reuse and modelling by saline composition with nanoparticles based on machine learning architectures.

Water is a necessary resource that enables the existence of all life forms, including humans. Freshwater usage has become increasingly necessary in recent years. Facilities for treating seawater are less dependable and effective. Deep learning methods have the ability to improve salt particle analysis in saltwater's accuracy and efficiency, which will enhance the performance of water treatment plants. This research proposes a novel technique in optimization of water reuse with nanoparticle analysis based on machine learning architecture. Here, the optimization of water reuse is carried out based on nanoparticle solar cell for saline water treatment and the saline composition has been analyzed using a gradient discriminant random field. Experimental analysis is carried out in terms of specificity, computational cost, kappa coefficient, training accuracy, and mean average precision for various tunnelling electron microscope (TEM) image datasets. The bright-field TEM (BF-TEM) dataset attained a specificity of 75%, kappa coefficient of 44%, training accuracy of 81%, and mean average precision of 61%, whereas the annular dark-field scanning TEM (ADF-STEM) dataset produced specificity of 79%, kappa coefficient of 49%, training accuracy of 85%, and mean average precision of 66% as compared with the existing artificial neural network (ANN) approach.

Performance assessment and multiobjective optimization of a biomass waste-fired gasification combined cycle for emission reduction.

The use of renewable fuels leads to reduction in the use of fossil fuels and environmental pollutants. In this study, the design and analysis of a CCPP based on the use of syngas produced from biomass is discussed. The studied system includes a gasifier system to produce syngas, an external combustion gas turbine and a steam cycle to recover waste heat from combustion gases. Design variables include syngas temperature, syngas moisture content, CPR, TIT, HRSG operating pressure, and PPTD. The effect of design variables on performance components such as power generation, exergy efficiency and total cost rate of the system is investigated. Also, through multi-objective optimization, the optimal design of the system is done. Finally, it is observed that at the final decisioned optimal point, the produced power is 13.4 MW, the exergy efficiency is 17.2%, and the TCR is 118.8 $/h.

Deep convolutional neural network with sine cosine algorithm based wastewater treatment systems.

Wastewater treatment systems are essential in today's business to meet the ever-increasing requirements of environmental regulations while also limiting the environmental impact of the sector's discharges. A new control and management information system is needed to handle the residual fluids. This study advises that Wastewater Treatment System (WWTS) operators use intelligent technologies that analyze data and forecast the future behaviour of processes. This method incorporates industrial data into the wastewater treatment model. Deep Convolutional Neural Network (DCNN) and Since Cosine Algorithm (SCA), two powerful artificial neural networks, were used to predict these properties over time. Remediation actions can be taken to ensure procedures are carried out in accordance with the specifications. Water treatment facilities can benefit from this technology because of its sophisticated process that changes feature dynamically and inconsistently. The ultimate goal is to improve the precision with which wastewater treatment models create their predictions. Using DCNN and SCA techniques, the Chemical Oxygen Demand (COD) in wastewater treatment system input and effluent is estimated in this study. Finally, the DCNN-SCA model is applied for the optimization, and it assists in improving the predictive performance. The experimental validation of the DCNN-SCA model is tested and the outcomes are investigated under various prospects. The DCNN-SCA model has achieved a maximum accuracy performance and proving that it outperforms compare with the prevailing techniques over recent approaches. The DCNN-SCA-WWTS model has shown maximum performance Under 600 data, DCNN-SCA-WWTS has a precision of 97.63%, a recall of 96.37%, a F score of 95.31%, an accuracy of 96.27%, an RMSE of 27.55%, and a MAPE of 20.97%.

Integral effects of brassinosteroids and timber waste biochar enhances the drought tolerance capacity of wheat plant.

Drought stress is among the major threats that affect negatively crop productivity in arid and semi-arid regions. Probably, application of some additives such as biochar and/or brassinosteroids could mitigate this stress; however, the mechanism beyond the interaction of these two applications is not well inspected. Accordingly, a greenhouse experiment was conducted on wheat (a strategic crop) grown under deficit irrigation levels (factor A) i.e., 35% of the water holding capacity (WHC) versus 75% of WHC for 35 days while considering the following additives, i.e., (1) biochar [BC, factor B, 0, 2%] and (2) the foliar application of 24-epibrassinolide [BR, factor C, 0 (control treatment, C), 1 (BR1) or 3 (BR2) µmol)]. All treatments were replicated trice and the obtained results were statistically analyzed via the analyses of variance. Also, heat-map conceits between measured variables were calculated using the Python software. Key results indicate that drought stress led to significant reductions in all studied vegetative growth parameters (root and shoot biomasses) and photosynthetic pigments (chlorophyll a, b and total contents) while raised the levels of oxidative stress indicators. However, with the application of BC and/or BR, significance increases occurred in the growth attributes of wheat plants, its photosynthetic pigments, especially the combined additions. They also upraised the levels of enzymatic and non-enzymatic antioxidants while decreased stress indicators. Furthermore, they increased calcium (Ca), phosphorus (P) and potassium (K) content within plants. It can therefore be deduced that the integral application of BR and BC is essential to mitigate drought stress in plants.

Controlling harmful algal blooms (HABs) by coagulation-flocculation-sedimentation using liquid ferrate and clay.

Harmful algal blooms (HABs) occur worldwide and threaten the quality of marine life, public health, and membrane facilities in Seawater Reverse Osmosis (SWRO) desalination plants. The effects of HABs on seawater desalination plants include extensive membrane fouling, increased coagulant consumption and plant shutdown. To determine how to mitigate such effects, this study assessed if low doses (0.01 mg/L, 0.10 mg/L, and 1.00 mg/L) of liquid ferrate (58% yield) and kaolin or montmorillonite clays alone could remove algal organic matter in coagulation-flocculation-sedimentation (CFS) pretreatment desalination systems. Results showed that 0.01 mg/L of liquid ferrate coagulant removed 42% of dissolved organic carbon (DOC), 52% of biopolymers (BP), 71% of algal cells, and 99.5% of adenosine triphosphate (ATP). At a dose of 0.01 mg/L, clays exhibited high removal of turbidity (up to 88%), BP (up to 80%) and algal cells (up to 67%). The combination of liquid ferrate (58% yield) as a coagulant with kaolin or montmorillonite clays as coagulant aids in CFS pretreatment led to 72% removal of DOC, 86% of BP, and 84% of algal cells with a fixed dose of 0.01 mg/L for each. Findings from this study can help SWRO plants improve the performance of pretreatment systems during algal bloom events by reducing the consumption of coagulants while also maintaining high removal efficiencies.

Advanced coagulation using in-situ generated liquid ferrate, Fe (VI), for enhanced pretreatment in seawater RO desalination during algal blooms.

Harmful algal blooms (HABs) are considered a major threat for seawater reverse osmosis (SWRO) plants. The presence of HABs in the raw feed water can cause increase of chemical consumption within the desalination plant, increase membrane fouling rate and might lead to plant shutdown. The removal of Algal Organic Matters (AOMs) during the pretreatment will help in increasing the membrane lifetime, reduce operation cost and increase the plant reliability. In this study, the efficiency of liquid ferrate and ferric chloride during coagulation on the removal of AOMs was investigated. The liquid ferrate was generated in-situ by wet oxidation of ferric iron using hypochlorite in a caustic medium. Two seawater models were employed, the first one contains 10 mg c/L of sodium alginate and the second one contains also 10 mg c/L of Chaetoceros affinis algae (CA). During the advanced coagulation, liquid ferrate proved to be more effective in removing AOM than ferric chloride, with an overall DOC removal of 90%, enabling 100% algal removal and the inactivation of 99.99% of the microorganisms. The results presented in this study highlights the efficiency of liquid ferrate as seawater pretreatment during the HABs events.