The optimal felling rate in the palm oil plantation system

Successful oil palm plantation should have high profit, clean and environmental friendly. Since oil palm trees have a long life and it takes years to be fully grown, controlling the felling rate of the oil palm trees is a fundamental challenge. It needs to be addressed in order to maximize oil production. However, a good arrangement of the felling of the oil palm trees may also affect the amount of carbon absorption. The objective of this study is to develop an optimal felling model of the oil palm plantation system taking into account both oil production and carbon absorption. The model facilitates in providing the optimal control of felling rate that results in maximizing both oil production and carbon absorption. With this aim, the model is formulated considering oil palm biomass, carbon absorption rate, oil production rate and the average prices of carbon and oil palm. A set of real data is used to estimate the parameters of the model and numerical simulation is conducted to highlight the application of the proposed model. The resulting parameter estimation that leads to an optimal control of felling rate problem is solved

Modeling of microbial approach in wastewater treatment process: a case study of mPHO in Taman Timor oxidation pond, Johor, Malaysia

In this study, we consider the application of biological based product mPHO that contains Phototrophic bacteria (PSB) for the degradation of bacteria Coliform (pollutant) in Taman Timor Oxidation Pond, Johor, Malaysia. A mathematical model is developed to describe the reaction between microorganism and pollutant. The model facilitates the determination of mPHO optimum amount for achieving the maximum pollutant decontamination in the oxidation pond. A partial differential equation model with coupled equation is developed, and the parameters of the model are estimated using the real data collected from the oxidation pond under study. The numerical simulations are also presented to illustrate the performance of proposed model

A direct probabilistic global search method for the solution of constrained optimal control problems

This research focuses on the development of a new direct stochastic algorithm to address the global optimization of the constrained optimal control problem where the interaction between state and control variables is governed by a system of ordinary differential equations. The objective of this method is to localize a globally optimal control curve in the feasible control space of the problem in such a way that the performance index attains its minimum value. The stochastic methodology is used on the development of the method. Thus, the resulting method is still effective when the complexity of the arising problems prohibits applying gradient-based methods. In this approach, the aforementioned control problem has first to be transformed into a nonlinear programming problem via a suitable discretization technique. The resulting problem is then solved using a stochastic method called Probabilistic Global Search Johor (PGSJ). The idea underpinning the PGSJ is to intelligently sample among potential solutions while no recombination or mutation operator is used. The sampling procedure is performed in accordance with some probability density functions (pdf) which are first initialized uniformly and then iteratively biased towards a globally optimal solution using the information obtained by evaluating the sampling points. After the PGSJ has been successfully implemented, it is found that it is able to arrive at an acceptable solution of the applied optimal control problems. The algorithm is also furnished with some theoretical supports verifying its convergence in probabilistic sense. In addition, some existing global stochastic methods which are based on using pdf are also applied on the optimal control problems where simulations reveal that the PGSJ method is superior to its competitors in terms of computation time and solution quality. These investigations lead to the extension of PGSJ into PGSJ-LS where LS indicates a line search operator added to the original method. These are then assessed and compared by applying them to a practical problem of controlling avian influenza H5N1 where it is verified that the PGSJ-LS performs slightly better than PGS

A hybrid optimization method for constrained optimal control problem

A new hybrid algorithm by integrating a nested partitions (NP) method with successive quadratic programming (SQP) is presented for global optimization of general optimal control problems involving lumped parameter system. The control parameterization technique first employed to reduce the control problem into a parameter selection problem. Then, in the global phase a vicinity of global optimizer is approximated by an appropriate NP method. Subsequently, the SQP algorithm in the local phase promotes the accuracy of final solution. The effectiveness of the hybrid NP–SQP algorithm is also illustrated by means of numerical simulations

A probabilistic algorithm for optimal control problem

In this paper we present a direct method for the numerical solution of the constrained optimal control problem when the gradient information is not available. At this aim, a new control parameterization based on Bernstein basis functions is suggested to convert control problem into nonlinear programing problem (NLP), and then a recently proposed stochastic algorithm called Probabilistic Global Search Johor (PGSJ) is considered for the solution of resultant NLP. The underlining idea of the PGSJ algorithm is to use probability density functions (PDF) to direct the search while no recombination operator is used. This algorithm along with the new Bernstein-based control parameterization (BCP) is compiled into BCP/PGSJ direct method to be applied to approximate the solution of the control problem up to the accuracy required. This method is lastly implemented while simulating some case studies which illustrate the efficiency of the method

Effect of endurance training on protein expression of CGI-58, ATGL and serum levels of insulin and glucose in streptozotocin-induced diabetic rats

IntrodutionThe purpose of this study was investigating the effect of endurance training on protein expression of CGI-58, ATGL and serum levels of insulin and glucose in streptozotocin-induced diabetic rats. Methods: 24 male Wistar rats were randomly assigned to three groups of eight, including diabetic group with endurance exercise (D-E), diabetic (D) and healthy control groups (Con). After induction of diabetic rats by injection streptozotocin was administered intraperitoneal , endurance exercise was applied for eight weeks, three sessions pre week in diabetic rats. Exercise intensity was equal to a speed of 21-25 m / min. The relative expression of CGI-58 and ATGL protein was measured with western blot technique and serum insulin and glucose levels were measured with a specialized kit. One-way ANOVA test was performed using SPSS-20 software and at a significance level less than 5%. Results: Results showed that ATGL and CGI-58 values were significantly different between the three groups (p <0.001). ATGL difference between the groups of diabetic group with endurance exercise with control (p = 0.001) and diabetic (p = 0.001)was significant. CGI-58 difference between the groups of diabetic group with endurance exercise with control (p = 0.001) and diabetic (p = 0.002) was significant. In addition, serum glucose and insulin levels decreased significantly after eight weeks of training (p <0.05). Conclusion: It seems that CGI-58 play a vital role in activating lipolysis by ATGL and increasing in CGI-58 leads to an increase in ATGL and ultimately leads to increased levels of intramuscular triglyceride oxidation and improved insulin resistance

Enhanced compact artificial bee colony

Challenges in many real-world optimization problems arise from limited hardware availability, particularly when the optimization must be performed on a device whose hardware is highly restricted due to cost or space. This paper proposes a new algorithm, namely Enhanced compact Artificial Bee Colony (EcABC) to address this class of optimization problems. The algorithm benefits from the search logic of the Artificial Bee Colony (ABC) algorithm, and similar to other compact algorithms, it does not store the actual population of tentative solutions. Instead, EcABC employs a novel probabilistic representation of the population that is introduced in this paper. The proposed algorithm has been tested on a set of benchmark functions from the CEC2013 benchmark suite, and compared against a number of algorithms including modern compact algorithms, recent population-based ABC variants and some advanced meta-heuristics. Numerical results demonstrate that EcABC significantly outperforms other state of the art compact algorithms. In addition, simulations also indicate that the proposed algorithm shows a comparative performance when compared against its population-based versions