Search results for: yield optimization

Authors: Hong-Ming Chen

Abstract:

This work considers the resolution of the Hull and White interest rate model with the jump process. A deterministic process is adopted to model the random behavior of interest rate variation as deterministic perturbations, which is depending on the time t. The Brownian motion and jumps uncertainty are denoted as the integral functions piecewise constant function w(t) and point function θ(t). It shows that the interest rate function and the yield function of the Hull and White interest rate model with jump process can be obtained by solving a nonlinear semi-infinite programming problem. A relaxed cutting plane algorithm is then proposed for solving the resulting optimization problem. The method is calibrated for the U.S. treasury securities at 3-month data and is used to analyze several effects on interest rate prices, including interest rate variability, and the negative correlation between stock returns and interest rates. The numerical results illustrate that our approach essentially generates the yield functions with minimal fitting errors and small oscillation.

Keywords: optimization, interest rate model, jump process, deterministic

Procedia PDF Downloads 134

Authors: Kostas Metaxiotis

Abstract:

Portfolio optimization is one of the most important topics in finance. This paper proposes a mean–variance–skewness (MVS) portfolio optimization model. Traditionally, the portfolio optimization problem is solved by using the mean–variance (MV) framework. In this study, we formulate the proposed model as a three-objective optimization problem, where the portfolio's expected return and skewness are maximized whereas the portfolio risk is minimized. For solving the proposed three-objective portfolio optimization model we apply an adapted version of the non-dominated sorting genetic algorithm (NSGAII). Finally, we use a real dataset from FTSE-100 for validating the proposed model.

Keywords: evolutionary algorithms, portfolio optimization, skewness, stock selection

Procedia PDF Downloads 159

Authors: Abdullah Özköse, Ahmet Tamkoç

Abstract:

The present study was conducted to determine the yield and yield components of smooth bromegrass lines under the environmental conditions of the Konya region during the growing seasons between 2011 and 2013. The experiment was performed in the randomized complete block design (RCBD) with four replications. It was found that the selected lines had a statistically significant effect on all the investigated traits, except for the main stem length and the number of nodes in the main stem. According to the two-year average calculated for various parameters checked in the smooth bromegrass lines, the main stem length ranged from 71.6 cm to 79.1 cm, the main stem diameter from 2.12 mm from 2.70 mm, the number of nodes in the main stem from 3.2 to 3.7, the internode length from 11.6 cm to 18.9 cm, flag leaf length from 9.7 cm to 12.7 cm, flag leaf width from 3.58 cm to 6.04 mm, herbage yield from 221.3 kg da^–1 to 354.7 kg da^–1 and hay yield from 100.4 kg da^–1 to 190.1 kg da^–1. The study concluded that the smooth bromegrass lines differ in terms of yield and yield components. Therefore, it is very crucial to select suitable varieties of smooth bromegrass to obtain optimum yield.

Keywords: semiarid region, smooth bromegrass, yield, yield components

Procedia PDF Downloads 250

Authors: Mohammad Reza Haj Seyed Hadi, Fereshteh Ghanepasand, Mohammad Taghi Darzi

Abstract:

The main objective of this study was to determine the effects of Nitrogen fixing bacteria and manure application on the seed yield and yield components in black cumin (Nigella sativa L.). The experiment was carried out at the RAN Research Station in Firouzkouh in 2012. A 4×4 factorial experiment, arranged in a randomized complete blocks designed with three replications. The treatments consisted of 4 level of nitrogen fixing bacteria (control, Azotobacter, Azospirillum and Azotobacter + Azospirillum) and 4 level of manure (0, 2.5, 5 and 7.5 ton ha-1). The present results have shown that the highest height, 1000 seeds weight, seed number per follicle, follicle yield, seed yield and harvest index were obtained after using Azotobacter and Azospirillum, simultaneously. Manure application only affects on follicle yield and by 5ton manure ha-1 the highest follicle yield obtained. Results of this investigation showed that the maximum seed yield obtained when Aotobacter+Azospirillum inoculated with black cumin seeds and 5 ton manure ha-1 applied. Combined application of nitrogen fixing bacteria and manure can be helpful in developing of production and yield in Black cumin.

Keywords: azotobacter, azospirillum, black cumin, yield, yield components

Procedia PDF Downloads 426

Authors: Serges Mendomo Meye, Li Guowei, Shen Zhenzhong, Gan Lei, Xu Liqun

Abstract:

Given the limitations of the original whale optimization algorithm (WAO) in local optimum and low convergence accuracy in truss structure optimization problems, based on the fundamental whale algorithm, an improved whale optimization algorithm (SWAO) based on information entropy is proposed. The information entropy itself is an uncertain measure. It is used to control the range of whale searches in path selection. It can overcome the shortcomings of the basic whale optimization algorithm (WAO) and can improve the global convergence speed of the algorithm. Taking truss structure as the optimization research object, the mathematical model of truss structure optimization is established; the cross-sectional area of truss is taken as the design variable; the objective function is the weight of truss structure; and an improved whale optimization algorithm (SWAO) is used for optimization design, which provides a new idea and means for its application in large and complex engineering structure optimization design.

Keywords: information entropy, structural optimization, truss structure, whale algorithm

Procedia PDF Downloads 214

Authors: Ramin Javadzadeh

Abstract:

The particle swarm optimization are Meta heuristic optimization method, which are used for clustering and pattern recognition applications are abundantly. These algorithms in multimodal optimization problems are more efficient than genetic algorithms. A major drawback in these algorithms is their slow convergence to global optimum and their weak stability can be considered in various running of these algorithms. In this paper, improved Particle swarm optimization is introduced for the first time to overcome its problems. The fuzzy cellular automata is used for improving the algorithm efficiently. The credibility of the proposed approach is evaluated by simulations, and it is shown that the proposed approach achieves better results can be achieved compared to the Particle swarm optimization algorithms.

Keywords: cellular automata, cellular learning automata, local search, optimization, particle swarm optimization

Procedia PDF Downloads 573

Authors: María L. Jalón, Feargal Brennan

Abstract:

A non-stationary stochastic optimization methodology is applied to an OWC (oscillating water column) to find the design that maximizes the wave energy extraction. Different temporal cycles are considered to represent the long-term variability of the wave climate at the site in the optimization problem. The results of the non-stationary stochastic optimization problem are compared against those obtained by a stationary stochastic optimization problem. The comparative analysis reveals that the proposed non-stationary optimization provides designs with a better fit to reality. However, the stationarity assumption can be adequate when looking at averaged system response.

Keywords: non-stationary stochastic optimization, oscillating water, temporal variability, wave energy

Procedia PDF Downloads 338

Authors: Liudmyla Koliechkina, Olena Dvirna

Abstract:

The statement of the multi-objective optimization problem on combinatorial configurations is formulated, and the approach to its solution is proposed. The problem is of interest as a combinatorial optimization one with many criteria, which is a model of many applied tasks. The approach to solving the multi-objective optimization problem on combinatorial configurations consists of two stages; the first is the reduction of the multi-objective problem to the single criterion based on existing multi-objective optimization methods, the second stage solves the directly replaced single criterion combinatorial optimization problem by the horizontal combinatorial method. This approach provides the optimal solution to the multi-objective optimization problem on combinatorial configurations, taking into account additional restrictions for a finite number of steps.

Keywords: discrete set, linear combinatorial optimization, multi-objective optimization, Pareto solutions, partial permutation set, structural graph

Procedia PDF Downloads 139

Authors: Yeshona Sewsynker

Abstract:

This study presents three microwave-assisted alkalic salt pretreatments to enhance delignification and enzymatic saccharification of corn cobs. The effects of process parameters of salt concentration (0-15%), microwave power intensity (0-800 W) and pretreatment time (2-8 min) on reducing sugar yield from corn cobs were investigated. Pretreatment models were developed with the high coefficient of determination values (R2>0.85). Optimization gave a maximum reducing sugar yield of 0.76 g/g. Scanning electron microscopy (SEM) and Fourier Transform Infrared analysis (FTIR) showed major changes in the lignocellulosic structure after pretreatment. A 7-fold increase in the sugar yield was observed compared to previous reports on the same substrate. The developed pretreatment strategy was effective for enhancing enzymatic saccharification from lignocellulosic wastes for microbial biofuel production processes and value-added products.

Keywords: pretreatment, lignocellulosic biomass, enzymatic hydrolysis, delignification

Procedia PDF Downloads 474

Authors: Sulemana Ibrahim

Abstract:

Food security remains a paramount global challenge, with vulnerable regions grappling with issues of hunger and malnutrition. This study embarks on a comprehensive exploration of data-driven strategies aimed at ameliorating food security in such regions. Our research employs a multifaceted approach, integrating data analytics to predict crop yields, optimizing supply chains, and enhancing food distribution networks. The study unfolds as a multi-dimensional analysis, commencing with the development of robust machine learning models harnessing remote sensing data, historical crop yield records, and meteorological data to foresee crop yields. These predictive models, underpinned by convolutional and recurrent neural networks, furnish critical insights into anticipated harvests, empowering proactive measures to confront food insecurity. Subsequently, the research scrutinizes supply chain optimization to address food security challenges, capitalizing on linear programming and network optimization techniques. These strategies intend to mitigate loss and wastage while streamlining the distribution of agricultural produce from field to fork. In conjunction, the study investigates food distribution networks with a particular focus on network efficiency, accessibility, and equitable food resource allocation. Network analysis tools, complemented by data-driven simulation methodologies, unveil opportunities for augmenting the efficacy of these critical lifelines. This study also considers the ethical implications and privacy concerns associated with the extensive use of data in the realm of food security. The proposed methodology outlines guidelines for responsible data acquisition, storage, and usage. The ultimate aspiration of this research is to forge a nexus between data science and food security policy, bestowing actionable insights to mitigate the ordeal of food insecurity. The holistic approach converging data-driven crop yield forecasts, optimized supply chains, and improved distribution networks aspire to revitalize food security in the most vulnerable regions, elevating the quality of life for millions worldwide.

Keywords: data-driven strategies, crop yield prediction, supply chain optimization, food distribution networks

Procedia PDF Downloads 35

Authors: R. M. Rizk-Allah

Abstract:

This paper presents co-evolutionary fruit fly optimization algorithm based on firefly algorithm (CFOA-FA) for solving unconstrained optimization problems. The proposed algorithm integrates the merits of fruit fly optimization algorithm (FOA), firefly algorithm (FA) and elite strategy to refine the performance of classical FOA. Moreover, co-evolutionary mechanism is performed by applying FA procedures to ensure the diversity of the swarm. Finally, the proposed algorithm CFOA- FA is tested on several benchmark problems from the usual literature and the numerical results have demonstrated the superiority of the proposed algorithm for finding the global optimal solution.

Keywords: firefly algorithm, fruit fly optimization algorithm, unconstrained optimization problems

Procedia PDF Downloads 510

Authors: Jia Zhang, Fengmei Yao, Yanjing Tan

Abstract:

The accurate estimation of crop yield is of great importance for the food security. In this study, a process-based mechanism model was modified to estimate yield of C4 crop by modifying the carbon metabolic pathway in the photosynthesis sub-module of the RS-P-YEC (Remote-Sensing-Photosynthesis-Yield estimation for Crops) model. The yield was calculated by multiplying net primary productivity (NPP) and the harvest index (HI) derived from the ratio of grain to stalk yield. The modified RS-P-YEC model was used to simulate maize yield in the Northeast China Plain during the period 2002-2011. The statistical data of maize yield from study area was used to validate the simulated results at county-level. The results showed that the Pearson correlation coefficient (R) was 0.827 (P < 0.01) between the simulated yield and the statistical data, and the root mean square error (RMSE) was 712 kg/ha with a relative error (RE) of 9.3%. From 2002-2011, the yield of maize planting zone in the Northeast China Plain was increasing with smaller coefficient of variation (CV). The spatial pattern of simulated maize yield was consistent with the actual distribution in the Northeast China Plain, with an increasing trend from the northeast to the southwest. Hence the results demonstrated that the modified process-based model coupled with remote sensing data was suitable for yield prediction of maize in the Northeast China Plain at the spatial scale.

Keywords: process-based model, C4 crop, maize yield, remote sensing, Northeast China Plain

Procedia PDF Downloads 332

Authors: Nagini Sabbineni, Rajini T. V. Kanth, B. V. Kiranmayee

Abstract:

India’s economy primarily depends on agriculture yield growth and their allied agro industry products. The agriculture yield prediction is the toughest task for agricultural departments across the globe. The agriculture yield depends on various factors. Particularly countries like India, majority of agriculture growth depends on rain water, which is highly unpredictable. Agriculture growth depends on different parameters, namely Water, Nitrogen, Weather, Soil characteristics, Crop rotation, Soil moisture, Surface temperature and Rain water etc. In our paper, lot of Explorative Data Analysis is done and various predictive models were designed. Further various regression models like Linear, Multiple Linear, Non-linear models are tested for the effective prediction or the forecast of the agriculture yield for various crops in Andhra Pradesh and Telangana states.

Keywords: agriculture yield growth, agriculture yield prediction, explorative data analysis, predictive models, regression models

Procedia PDF Downloads 278

Authors: Pham Van Nha, Le Cam Binh

Abstract:

Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.

Keywords: analysis of optimization, artificial intelligence based optimization, optimization for learning and data analysis, global optimization

Procedia PDF Downloads 181

Authors: Ndana Mohammed, Abdullahi Musa Sabo

Abstract:

The oils used in this work were extracted from seeds of Ricinuscommunis, Heaveabrasiliensis, Gossypiumhirsutum, Azadirachtaindica, Glycin max and Jatrophacurcasby solvent extraction method using n-hexane, and gave the yield of 48.00±0.00%, 44.30±0.52%, 45.50±0.64%, 47.60±0.51%, 41.50±0.32% and 46.50±0.71% respectively. However these feed stocks are highly challenging to trans-esterification reaction because they were found to contain high amount of free fatty acids (FFA) (6.37±0.18, 17.20±0.00, 6.14±0.05, 8.60±0.14, 5.35±0.07, 4.24±0.02mgKOH/g) in order of the above. As a result, two-stage trans-esterification reactions process was used to produce biodiesel; Acid esterification was used to reduce high FFA to 1% or less, and the second stage involve the alkaline trans-esterification/optimization of process condition to obtain high yield quality biodiesel. The salient features of this study include; characterization of oils using AOAC, AOCS standard methods to reveal some properties that may determine the viability of sample seeds as potential feed stocks for biodiesel production, such as acid value, saponification value, Peroxide value, Iodine value, Specific gravity, Kinematic viscosity, and free fatty acid profile. The optimization of process parameters in biodiesel production was investigated. Different concentrations of alkaline catalyst (KOH) (0.25, 0.5, 0.75, 1.0 and 1.50w/v, methanol/oil molar ratio (3:1, 6:1, 9:1, 12:1, and 15:1), reaction temperature (500 C, 550 C, 600 C, 650 C, 700 C), and the rate of stirring (150 rpm,225 rpm,300 rpm and 375 rpm) were used for the determination of optimal condition at which maximum yield of biodiesel would be obtained. However, while optimizing one parameter other parameters were kept fixed. The result shows the optimal biodiesel yield at a catalyst concentration of 1%, methanol/oil molar ratio of 6:1, except oil from ricinuscommunis which was obtained at 9:1, the reaction temperature of 650 C was observed for all samples, similarly the stirring rate of 300 rpm was also observed for all samples except oil from ricinuscommunis which was observed at 375 rpm. The properties of biodiesel fuel were evaluated and the result obtained conformed favorably to ASTM and EN standard specifications for fossil diesel and biodiesel. Therefore biodiesel fuel produced can be used as substitute for fossil diesel. The work also reports the result of the study on the evaluation of the effect of the biodiesel storage on its physicochemical properties to ascertain the level of deterioration with time. The values obtained for the entire samples are completely out of standard specification for biodiesel before the end of the twelve months test period, and are clearly degraded. This suggests the biodiesels from oils of Ricinuscommunis, Heaveabrasiliensis, Gossypiumhirsutum, Azadirachtaindica, Glycin max and Jatrophacurcascannot be stored beyond twelve months.

Keywords: biodiesel, characterization, esterification, optimization, transesterification

Procedia PDF Downloads 395

Authors: Sait Ali Uymaz, Gülay Tezel

Abstract:

This paper presents the comparison results on the performance of the Cuckoo Search (CS) algorithm for constrained optimization problems. For constraint handling, CS algorithm uses penalty method. CS algorithm is tested on thirteen well-known test problems and the results obtained are compared to Particle Swarm Optimization (PSO) algorithm. Mean, best, median and worst values were employed for the analyses of performance.

Keywords: cuckoo search, particle swarm optimization, constrained optimization problems, penalty method

Procedia PDF Downloads 528

Authors: T. Gomes, J. Manzi

Abstract:

The inclusion of stirring systems in the calculation and optimization procedures has been undergone a significant lack of attention, what it can reflect in the results because such systems provide an additional energy to the process, besides promote a better distribution of mass and energy. This is meaningful for the reactive systems, particularly for the Continuous Stirred Tank Reactor (CSTR), for which the key variables and parameters, as well as the operating conditions of stirring systems, can play a pivotal role and it has been showed in the literature that neglect these factors can lead to sub-optimal results. It is also well known that the sole use of the First Law of Thermodynamics as an optimization tool cannot yield satisfactory results, since the joint use of the First and Second Laws condensed into a procedure so-called entropy generation minimization (EGM) has shown itself able to drive the system towards better results. Therefore, the main objective of this paper is to determine the effects of key parameters of the stirring system in the optimization procedures by means of EGM applied to the reactive systems. Such considerations have been possible by dimensional analysis according to Rayleigh and Buckingham's method, which takes into account the physical and geometric parameters and the variables of the reactive system. For the simulation purpose based on the production of propylene glycol, the results have shown a significant increase in the conversion rate from 36% (not-optimized system) to 95% (optimized system) with a consequent reduction of by-products. In addition, it has been possible to establish the influence of the work of the stirrer in the optimization procedure, in which can be described as a function of the fluid viscosity and consequently of the temperature. The conclusions to be drawn also indicate that the use of the entropic analysis as optimization tool has been proved to be simple, easy to apply and requiring low computational effort.

Keywords: stirring systems, entropy, reactive system, optimization

Procedia PDF Downloads 227

Authors: Azin Nasrollah Zadeh

Abstract:

A factorial experiment was applied to evaluate the effect of chemical and biological fertilizer on yield, total nitrogen uptake and NHI of rice. Four biological treatments including:(M1:no fertilizer),( M2:10 ton/ha cow dung ),(M3:20 ton/ha cow dung) and (M4:5 ton/ha azolla compost) and four chemical fertilizer treatments including: (S1: no fertilizer),(S2:40 kg N /ha),(S3:60 kg N /ha) and ( S4:80 kg N /ha ) were compared. Results showed that highest rate of yield (3387 kg/ha) and total nitrogen uptake (81.4 kg/ha) were reached the highest value at M4. Among the chemical fertilizers the highest grain yield (3373 kg/ha) and total nitrogen uptake (87.7) belonged to highest nitrogen level (S4).Also biological and chemical fertilizers were no significant on Harvest index (NHI). Interaction effect of chemical × biological fertilizers didn't show significant difference between all parameters except of yield, as the most grain yield were obtained in M4S4. So it can be concluded that using of bioilogical fertilizers at appropriate rate and type, considering plant requirement, may improve grain yield, nitrogen uptake and use efficiency in rice.

Keywords: azolla, fertilizer, nitrogen uptake, rice, yield

Procedia PDF Downloads 269

Authors: A. S. Talaab, Safaa, A. Mahmoud, Hanan S. Siam

Abstract:

A field experiment was conducted during the winter season of 2013/2014 in the barley production area of Dakhala – New Valley Governorate, Egypt to assess the effect of nitrogen rate and time of N fertilizer application on barley grain yield, yield components and N use efficiency of barley and their association with grain yield. The treatments consisted of three levels of nitrogen (0, 70 and 100 kg N/acre) and five application times. The experiment was laid out as a randomized complete block design with three replication. Results revealed that barley grain yield and yield components increased significantly in response to N rate. Splitting N fertilizer amount at several times result in significant effect on grain yield, yield components, protein content and N uptake efficiency when compared with the entire N was applied at once. Application of N at rate of 100 kg N/acre resulted in accumulation of nitrate in the subsurface soil > 30cm. When N application timing considered, less NO3 was found in the soil profile with splitting N application compared with all preplans application.

Keywords: nitrogen use efficiency, splitting N fertilizer, barley, NO3

Procedia PDF Downloads 286

Authors: Simbiat O. Ayilara-Akande, Soji Fakoya

Abstract:

Quality and yield are always the target of farmers, including mushroom farmers. This study investigated how better Pleurotus pulmonarius can be obtained with the induction of mutagens into the process of spawn production in order to improve both the quality and the yield. Mushroom spawns were treated with ultraviolet radiation (UV) and hydroxylamine hydrochloride (HA) at different exposure times (2, 6, and 10 minutes) and different concentrations (10, 30, and 50Mm), respectively. The treated spawns were used to cultivate mushrooms on five substrates in the family of Gramineae viz: sorghum, rice, bamboo, sugarcane, and corn straws. Matured fruit bodies were harvested after a few weeks, and their parameters were taken and recorded. This study reveals a significant yield increase in mushroom grown on all the substrates when treated with ultraviolet radiation (UV) for 10 minutes and 6 minutes, respectively. Mushroom spawns treated with hydroxylamine hydrochloride showed a negative correlation in the yield with an increased in mutagen concentration. Hence, Ultraviolet light could be employed to enhance the quality and yield of mushroom production.

Keywords: mushroom, protein, mutagens, yield

Procedia PDF Downloads 116

Authors: Tea Poklepović, Zdravka Aljinović, Branka Marasović

Abstract:

Due to the importance of yield curve and its estimation it is inevitable to have valid methods for yield curve forecasting in cases when there are scarce issues of securities and/or week trade on a secondary market. Therefore in this paper, after the estimation of weekly yield curves on Croatian financial market from October 2011 to August 2012 using Nelson-Siegel and Svensson models, yield curves are forecasted using Vector auto-regressive model and Neural networks. In general, it can be concluded that both forecasting methods have good prediction abilities where forecasting of yield curves based on Nelson Siegel estimation model give better results in sense of lower Mean Squared Error than forecasting based on Svensson model Also, in this case Neural networks provide slightly better results. Finally, it can be concluded that most appropriate way of yield curve prediction is neural networks using Nelson-Siegel estimation of yield curves.

Keywords: Nelson-Siegel Model, neural networks, Svensson Model, vector autoregressive model, yield curve

Procedia PDF Downloads 288

Authors: S. Bangphan, P. Bangphan, C. Ketsombun, T. Sammana

Abstract:

Optimization of response surface methodology (RSM) was employed to study the effects of three factor (rubber of clearance, spindle of speed, and rice of moisture) in brown rice peeling machine of the optimal good rice yield (99.67, average of three repeats). The optimized composition derived from RSM regression was analyzed using Regression analysis and Analysis of Variance (ANOVA). At a significant level α=0.05, the values of Regression coefficient, R2 adjust were 96.55% and standard deviation were 1.05056. The independent variables are initial rubber of clearance, spindle of speed and rice of moisture parameters namely. The investigating responses are final rubber clearance, spindle of speed and moisture of rice.

Keywords: brown rice, response surface methodology (RSM), peeling machine, optimization, paddy husker

Procedia PDF Downloads 548

Authors: V. Nagaraju, G. Murali, Nagarjunavarma Ganna, Atluri Pavan Kalyan, N. Sree Sai Ganesh, V. S. V. S. Badrinath

Abstract:

The present work focuses on the development of a mathematical model for the yield obtained by single slope solar still incorporated with cylindrical pipes filled with sand. The mathematical results obtained were validated with the experimental results for the 3 cm of water level at the basin. The mathematical model and results obtained with the experimental investigation are within 11% of deviation. The theoretical model to predict the yield obtained due to the capillary effect was proposed first. And then, to predict the total yield obtained, the thermal effect model was integrated with the capillary effect model. With the obtained results, it is understood that the yield obtained is more in the case of solar stills with sand-filled cylindrical pipes when compared to solar stills without sand-filled cylindrical pipes. And later model was used for predicting yield for 1 cm and 2 cm of water levels at the basin. And it is observed that the maximum yield was obtained for a 1 cm water level at the basin. It means solar still produces better yield with the lower depth of water level at the basin; this may be because of the availability of more space in the sand for evaporation.

Keywords: solar still, cylindrical pipes, still efficiency, mathematical modeling, capillary effect model, yield, solar desalination

Procedia PDF Downloads 99

Authors: Bahiru Tsegaye, Chandrajit Balomajumder, Partha Roy

Abstract:

The limited supply and related negative environmental consequence of fossil fuels are driving researcher for finding sustainable sources of energy. Lignocellulose biomass like sorghum straw is considered as among cheap, renewable and abundantly available sources of energy. However, lignocellulose biomass conversion to bioenergy like bioethanol is hindered due to the reluctant nature of lignin in the biomass. Therefore, removal of lignin is a vital step for lignocellulose conversion to renewable energy. The aim of this study is to optimize microwave pretreatment conditions using design expert software to remove lignin and to release maximum possible polysaccharides from sorghum straw for efficient hydrolysis and fermentation process. Sodium hydroxide concentration between 0.5-1.5%, v/v, pretreatment time from 5-25 minutes and pretreatment temperature from 120-2000C were considered to depolymerize sorghum straw. The effect of pretreatment was studied by analyzing the compositional changes before and after pretreatments following renewable energy laboratory procedure. Analysis of variance (ANOVA) was used to test the significance of the model used for optimization. About 32.8%-48.27% of hemicellulose solubilization, 53% -82.62% of cellulose release, and 49.25% to 78.29% lignin solubilization were observed during microwave pretreatment. Pretreatment for 10 minutes with alkali concentration of 1.5% and temperature of 1400C released maximum cellulose and lignin. At this optimal condition, maximum of 82.62% of cellulose release and 78.29% of lignin removal was achieved. Sorghum straw at optimal pretreatment condition was subjected to enzymatic hydrolysis and fermentation. The efficiency of hydrolysis was measured by analyzing reducing sugars by 3, 5 dinitrisylicylic acid method. Reducing sugars of about 619 mg/g of sorghum straw were obtained after enzymatic hydrolysis. This study showed a significant amount of lignin removal and cellulose release at optimal condition. This enhances the yield of reducing sugars as well as ethanol yield. The study demonstrates the potential of microwave pretreatments for enhancing bioethanol yield from sorghum straw.

Keywords: cellulose, hydrolysis, lignocellulose, optimization

Procedia PDF Downloads 244

Authors: Ya-Li Tsai

Abstract:

Nitrogen is a vital element crucial for crop growth, significantly influencing crop yield. In rice cultivation, farmers often apply substantial nitrogen fertilizer to maximize yields. However, excessive nitrogen application increases the risk of lodging and pest infestation, leading to yield losses. Additionally, conventional flooded irrigation methods consume significant water resources, necessitating precise agricultural and intelligent water management systems. In this study, it leveraged physiological data and field images captured by unmanned aerial vehicles, considering fertilizer treatment and irrigation as key factors. Statistical models incorporating rice physiological data, yield, and vegetation indices from image data were developed. Missing physiological data were addressed using multiple imputation and regression methods, and regression models were established using principal component analysis and stepwise regression. Target nitrogen accumulation at key growth stages was identified to optimize fertilizer application, with the difference between actual and target nitrogen accumulation guiding recommendations for ear dressing dosage. Field experiments conducted in 2022 validated the recommended ear dressing dosage, demonstrating no significant difference in final yield compared to traditional fertilizer levels under alternate wetting and drying irrigation. These findings highlight the efficacy of applying recommended dosages based on fertilizer decision models, offering the potential for reduced fertilizer use while maintaining yield in rice cultivation.

Keywords: intelligent fertilizer management, nitrogen top and ear dressing fertilizer, rice, yield optimization

Procedia PDF Downloads 33

Authors: Yen-Hui Chen, Terry Walker

Abstract:

As the world experiences an energy crisis, investing in sustainable energy resources is a pressing mission for many countries. Microalgae-derived biodiesel has attracted intensive attention as an important biofuel, and microalgae Chlorella protothecoides lipid is recognized as a renewable source for microalgae-derived biodiesel production. Supercritical carbon dioxide (SC-CO₂) is a promising green solvent that may potentially substitute the use of organic solvents for lipid extraction; however, the efficiency of SC-CO₂ extraction may be affected by many variables, including temperature, pressure and extraction time individually or in combination. In this study, response surface methodology (RSM) was used to optimize the process parameters, including temperature, pressure and extraction time, on C. protothecoides lipid yield by SC-CO₂ extraction. A second order polynomial model provided a good fit (R-square value of 0.94) for the C. protothecoides lipid yield. The linear and quadratic terms of temperature, pressure and extraction time—as well as the interaction between temperature and pressure—showed significant effects on lipid yield during extraction. The optimal lipid yield from the model was predicted as the temperature of 59 °C, the pressure of 350.7 bar and the extraction time 2.8 hours. Under these conditions, the experimental lipid yield (25%) was close to the predicted value. The principal fatty acid methyl esters (FAME) of C. protothecoides lipid-derived biodiesel were oleic acid methyl ester (60.1%), linoleic acid methyl ester (18.6%) and palmitic acid methyl ester (11.4%), which made up more than 90% of the total FAMEs. In summary, this study indicated that RSM was useful to characterize the optimization the SC-CO₂ extraction process of C. protothecoides lipid yield, and the second-order polynomial model could be used for predicting and describing the lipid yield very well. In addition, C. protothecoides lipid, extracted by SC-CO₂, was suggested as a potential candidate for microalgae-derived biodiesel production.

Keywords: Chlorella protothecoides, microalgal lipids, response surface methodology, supercritical carbon dioxide extraction

Procedia PDF Downloads 422

Authors: Takahiro Hino, Michiharu Maeda

Abstract:

Particle swarm optimization (PSO) is an optimization approach that achieves the social model of bird flocking and fish schooling. PSO works in continuous space and can solve continuous optimization problem with high quality. Set-based particle swarm optimization (SPSO) functions in discrete space by using a set. SPSO can solve combinatorial optimization problem with high quality and is successful to apply to the large-scale problem. In this paper, we present an algorithm of SPSO with status memory to decide the position based on the previous position for solving traveling salesman problem (TSP). In order to show the effectiveness of our approach. We examine SPSOSM for TSP compared to the existing algorithms.

Keywords: combinatorial optimization problems, particle swarm optimization, set-based particle swarm optimization, traveling salesman problem

Procedia PDF Downloads 514

Authors: Xiangtuo Chen, Paul-Henry Cournéde

Abstract:

Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

Procedia PDF Downloads 209

Authors: Dawit Hailekrios Hailu

Abstract:

The main objective of this study is to predict the sediment yield of the Jido River Watershed. Jido River is the largest tributary and covers around 50% of the total catchment area of Lake Shala. This research is undertaken to analyze the sediment yield of the catchments, transport capacity of the streams and sediment deposition rates of Jido River, which is located in the Sub-basin of Shala Lake, Rift Valley Basin of Ethiopia. The input data were Meteorological, Hydrological, land use/land cover maps and soil maps collected from concerned government offices. The sediment yield of Jido River and sediment change of the streams discharging into the Shala Lake were modeled.

Keywords: sediment yield, watershed, simulation, calibration

Procedia PDF Downloads 35

Authors: Ayhan Aydoğdu, Ercan Ceyhan, Ali Kahraman, Nursel Çöl

Abstract:

This research was conducted to determine effects of plant densities on seed yield and some agricultural characteristics of pea variety- Jofs in Konya ecological conditions during 2012 vegetation period. The trial was set up according to “Randomized Blocks Design” with three replications. The material “Jofs” pea variety was subjected to 3-row spaces (30, 40 and 50 cm) and 3-row distances (5, 10 and 15 cm). According to the results, difference was shown statistically for the effects of row spaces and row distances on seed yield. The highest seed yield was 2582.1 kg ha-1 on 30 cm of row spaces while 2562.2 kg ha-1 on 15 cm of distances. Consequently, the optimum planting density was determined as 30 x 15 cm for Jofs pea variety growing in Konya.

Keywords: pea, row space, row distance, seed yield

Procedia PDF Downloads 541