Technological and economic analyses on power generation from the waste heat in a modified aluminum smeltingpot

Current pot ventilation system is designed in a conservative manner to prevent hazardous gases from escaping aluminum smelting pots. Reduction of pot draft is an initiative of both efficiently using waste heat in the exhaust gas and significantly reducing fan power of the ventilation system. This work presents a systematic analysis on the reduction of pot draft and the consequent problem, ie, fugitive emissions from the smelting pot. Numerical models with different length scales are developed to simulate the fluid flow and heat transfer in pots and potroom. The superstructure of a typical aluminum smelting pot is successfully modified to maintain the pot tightness in the reducing pot draft. The results show that the current pot draft could be reduced by 50% at least using the developed technology. The techno-economic analysis of power generation from the waste heat in aluminum smelting pot is made based on organic Rankine cycle (ORC), and the potential benefits of using the pot exhaust gas of both normal and 50% reduced flow rates are estimated. It is found that the levelized energy cost of the optimized ORC system using 50% reduced exhaust gas is 0.048 $/kWh with a system's lifespan of 10 years and 0.038 $/kWh with a lifespan of 15 years while the payback time of investment is 5.2 years.     

Optimization of biohydrogen production using acid pretreated corn stover hydrolysate followed by nickel nanoparticle addition

The dilute acid hydrolysis using corn stover (CS) to produce reducible sugars was optimized by the response surface methodology. The electron-equivalent balances of the main metabolites during the dark fermentation (DF) using acid hydrolysate were investigated to identify the evolutions of the electron sinks over the course of DF. The additions of nickel ion and Ni⁰ nanoparticles (NPs) were found to effectively enhance the hydrogen production at experimental conditions. The optimal condition (HCl 2.5 wt%, hydrolyzing duration 105 minutes, pH=5, S/B=3.5, Ni⁰ NPs=10 mg/L^-1) was achieved with Y_H2/S reaching 1.18 (mol.mol^-1-glucose). The Y_H2/S increased from 0.7 (mol.mol^-1-glucose) to 1.18 (mol.mol^-1-glucose) reaching 40% hydrogen yield increase when Ni⁰ NPs was added to the fermentation broth. Among the investigated significant soluble metabolites, the butyric acid was found to serve as the largest e-sink in the electron-equivalent balance. The additions of Ni⁰ NPs at low level (below 10 mg/L) were found to appreciably increase the hydrogen production. The increased pH and substrate to biomass ratio were found to skew the metabolic balance from hydrogen production to the biosynthesis (an increase of biomass). The proposed anaerobic digestion model with consideration of the inhibitory factors model presents a good agreement with the experimental data. The chemical addition such as nickel ions, Ni⁰ NPs was found to be a practical approach in enhancing biohydrogen production using CS acid hydrolysate as cultivation broth.     

Optimal design of autonomous desalination with storage energy system using SSO algorithm

Sustainable power sources, that is, the energy produced as of hydro control, biomass, wind, geothermal, sunlight, and sea resources deliberated as innovative choice intended to create clean energy and crisp water. The majority of the nations have challenged issues proceeding freshwater and power creation, which tends to the utilization of sustainable power source-controlled desalination frameworks. In this work expanding the clean water accessibility that satisfies the load need, Hybrid Renewable Energy Systems (HRES) on the basis of Reverse Osmosis Desalination (ROD) is structured and then displayed. HRES is getting to be well known for power applications because of advances in sustainable power sources. Here, an innovative calculation based on Social Spider Optimization (SSO) is aimed at explaining the required optimization tasks. The technique referenced is turned out to be powerful, utilizing sustainable power source framework. The proposed system is contrasted with a genuine contextual analysis in the eastern part of Iran, Canary Islands and outcomes demonstrate that it has been successfully utilized thinking about both power quality and cost. Also, the outcomes acquired by the suggested technique are very encouraging to outline the capability and strength of the introduced methodology.     

Design,modeling, and optimization of a dual reflector parabolic trough concentration system

The aim of the present work is to enhance the thermal management avoiding the high-thermal stress on the outer surface of the parabolic trough receiver (PTR) derived from nonuniform concentrated solar flux distribution. A parabolic trough concentrating (PTC) system with second homogenizing reflector (HR) is numerically designed and optimized to ensure a uniform concentrated solar flux on the PTR walls. For this purpose, a three-dimensional optical model has been developed to analyze quantitatively the improvement made by the HR using the optical efficiency and qualitatively basing on the uniformity of the solar flux density distribution over the entire surface of the PTR. The validation of the numerical tool is presented, and the algorithm of the design process has been proposed and detailed. As a preliminary trait, it was revealed that the peak of the designed system performance is achieved with a rim angle of 68° avoiding simultaneously the aberration and the blocking effects. Despite the optical efficiency decrease by about 7% compared with the conventional PTC design, the uniformity of the solar flux distribution has been strongly improved such that the maximum local solar flux density gradient is decreased from 80 to 11 kW/m² equivalent to a decrease of 86.25% with respect to the conventional PTC and the average local density is about 25.5 kW/m².     

Parameters extraction of PEMFC's model using manta rays foraging optimizer

In this article, a recently developed bio-inspired based manta rays foraging optimizer (MRFO) is attempted for reliable and accurate extraction of the model uncertain parameters of proton exchange membrane fuel cells (PEMFCs). The parameter estimation is formulated as a non-linear optimization problem subject to set of restrictions. The great development and tremendous revolution of computation heuristic-based algorithms are the impetus of the authors to apply the MRFO to solve this constrained optimization problem resulting in a precise PEMFC model. Three case studies of typical field PEMFC stacks namely Ballard type Mark V, NedStack type PS6, and Horizon type H-12. Various I to V datasets are demonstrated to appraise the performance of MRFO among other recent optimizers available in the literature. To be objective and for sake of quantifications, the best scores of minimum fitness values are 0.8533, 2.1360, and 0.0966 for the later said PEMFC stacks, correspondingly. At a later stage, production of various characteristics under varying operating conditions such as changeable cell temperature and regulating pressures are established using the generated best values of PEMFCs model. Further calculations of statistical indices are performed to validate the robustness of obtained results by the MRFO. Through comprehensive performance assessments, it can be confirmed that MRFO is very promising tool for the effective extraction of PEMFCs' model and suggested to be applied for solving other engineering problems.     

Cross-regional integrated energy system scheduling optimization model considering conditional value at risk

Integrated energy system is a very important way to improve energy efficiency. Based on the combined heating cooling and power system, combined with energy storage equipment, a cross-regional integrated energy system scheduling optimization problem is studied. An integrated energy system scheduling optimization model is established that meets the requirements of electrical, heating, and cooling load under a variety of energy sources while both considering the interaction of electrical, heating, and cooling load between regions, and complementation of them within one region. Meanwhile, the value at risk (VaR) theory is introduced and the operating constraints of equipment in the integrated energy system fully considered, the integrated energy system scheduling model with VaR is established. The example shows that the model can realize multi-type electrical, heating, and cooling load optimized by schedule across regions under the premise of satisfying the balance of energy supply and demand, which can reduce the system operation cost. The sensitivity analysis of the minimum expected cost and the influencing factors of conditional VaR is carried out to verify the validity and feasibility of the proposed model.

• An integrated energy system scheduling optimization model is established that meets the requirements of electrical, heating, and cooling load under a variety of energy sources while both considering the interaction of electrical, heating, and cooling load between regions, and complementation of them within one region.
• By using the conditional value at risk theory to consider various types of the integrated energy system complements and evaluates the operational risk of the system under optimal operating conditions of the system.
• The total cost of system scheduling operation is proportional to the storage capacity, which is inversely proportional to the heat storage capacity and inversely proportional to the pipeline capacity within a certain interval.

     

Optimal design of an integrated renewable-storage energy system in a mixed-use building

The rise of mixed-use buildings contributes to the sustainable development of cities but are still met with challenges in energy management due to the lack of energy efficiency and sustainability guidelines. The use of integrated renewable-storage energy systems is a more beneficial solution to this problem over individual solutions; however, most design studies only focused on single-type buildings. Thus, this study aims to optimally design an integrated energy system for mixed-use buildings using HOMER Grid. The objective is to minimize the net present costs, subject to capacity limits, energy balances, and operational constraints. Economic metrics were used to evaluate and compare the proposed system to the varying design cases such as business-as-usual, stand-alone renewable source, and stand-alone energy storage. The case study considered a mixed-use building in a tropical area, with a solar photovoltaic system as the renewable energy source and lithium-ion battery as the energy storage system technology. The results show that the integrated system is the most financially attractive design case. It has a levelized cost of electricity of 0.1384 US$ kWh⁻¹, which is significantly less than the 0.2580 US$ kWh⁻¹ baseline. The system also provides electricity cost savings of 294 698 US$ y⁻¹, excess electricity of 35 746 kWh, and carbon emission reduction of 550 tons annually for a mixed-use building with daily average consumption of 4557-kWh and 763-kW peak demand.     

Optimal parameter estimation for PEMFC using modified monarch butterfly optimization

The present study develops a new optimization method called monarch butterfly optimization algorithm for optimal parameter estimation of the polymer electrolyte membrane fuel cell (PEMFC). After designing the proposed methodology, it is implemented to 250 MW PEMFC stack under different operating conditions to show the system efficiency and the results are compared with some state-of-the-art methods including Grass Fibrous Root Optimization Algorithm, hybrid Teaching Learning Based Optimization-Differential Evolution Algorithm, and the basic MBO algorithm. Two operational conditions in 3/5 bar and 80°C and 2.5/3 bar, 70°C are used for model verification. The main idea is to minimize the sum of square error (SSE) between the estimated and the actual data. Simulation results in the first condition give an SSE of value 7.277667729 with 9.28434e−16 SD value and in the second condition, an SSE of value 4.52810115 with 0.043581628 standard deviations has been reached as the minimum value among the other compared methods that indicate the accuracy and the robustness of the suggested method toward the analyzed methods. The algorithm also gives a convergence speed of 540 iterations and 370 iterations for conditions 1 and 2, respectively that are the fastest in the study.     

Thermo-economic analysis and optimization of a combined Organic Rankine Cycle (ORC) system with LNG cold energy and waste heat recovery of dual-fuel marine engine

A combined Organic Rankine Cycle (ORC) system with liquefied nature gas (LNG) cold energy and dual-fuel (DF) marine engine waste heat utilization was proposed. Engine exhaust gas and engine jacket cooling water were adopted as parallel heat sources. Thermo-economic analyses of the proposed system with 32 working fluids combinations were performed. Two objective functions covering thermal efficiencies and economic index were employed for performance evaluation. Afterward, the effects of operation pressure on the objective functions were investigated. Finally, the optimal conditions were obtained from the Pareto front with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) method. The results show that the proposed ORC system has better energy recovery performances than the parallel ORC system. R1150-R600a-R290, R1150-R601a-R600a, and R170-R601-R290 are determined as the three most promising working fluids combinations. Under optimized conditions, the output power range is 199.97 to 218.51 kW, the energy efficiency range is 13.64% to 15.62%, and the exergy efficiency range is 25.29% to 27.3%. The payback period ranges from 8.36 to 8.74 years. The working fluids selection helps to reduce the exergy destruction of intermediate heat exchanger, which could be up to 30.59%.     

Fuzzy modeling and particle swarm optimization for determining the optimal operating parameters to enhance the bio-methanol production from sugar cane bagasse

This work aims to maximize the production of bio-methanol from sugar cane bagasse through pyrolysis. The maximum value of the bio-methanol yield can be obtained as soon as the optimal operating parameters in a pyrolysis batch reactor are well defined. Using the experimental data, the fuzzy logic technique is used to build a robust model that describes the yield of bio-methanol production. Then, Particle Swarm Optimization (PSO) algorithm is utilized to estimate the optimal values of the operating parameters that maximize the bio-methanol yield. Three different operating parameters influence the yield of bio-methanol from sugar cane bagasse through pyrolysis. The controlling parameters are considered as the reaction temperature (°C), reaction time (min), and nitrogen flow (L/min). Accordingly, during the optimization process, these parameters are used as the decision variables set for the PSO optimizer in order to maximize the yield of bio-methanol, which is considered as a cost function. The results demonstrated a well-fitting between the fuzzy model and the experimental data compared with previous predictions obtained by an artificial neural network (ANN) model. The mean square errors of the model predictions are 0.11858 and 0.0259, respectively, for the ANN and fuzzy-based models, indicating that fuzzy modeling increased the prediction accuracy to 78.16% compared with ANN. Based on the built model, the PSO optimizer accomplished a substantial improvement in the yield of bio-methanol by 20% compared to that obtained experimentally, without changing system design or the materials used.