Enhanced biogas production from anaerobic co-digestion of palm oil mill effluent using solar-assisted bioreactor

Anaerobic co-digestion (ACoD), a sustainable green technology, presents an outstanding opportunity for energy conversion and environmental pollution control. It has become a core method of treating organic wastes on account of its environmental and economic benefits of energy production. Prolonged start-up period, slow reactions, and methanogenesis are highly inhibited in the ACoD process which prevents enhancement in energy production. Instead, oxidization by hydrogen peroxide (OHP) had substantial impacts on biological break down and enhancing biogas production by ACoD methods. Again, lack of nitrogenous substrate and buffering potential has been known as an obstruction for the treatment of POME in the ACoD process. The key objective of this study was to investigate the potential of the ACoD for palm oil mill effluent (POME) treatment with cattle manure (CM) in a solar-assisted bioreactor (SABr) to produce enhanced biogas. Finally, this study developed the artificial neural network (ANN) model which is an appropriate and uncomplicated modeling approach for ACoD applications to predict the outcomes of biogas production using experimental data. Standard American Public Health Association (APHA) methods analyzed the characterization of the samples. The solar panel first converted solar radiation into electricity, which warmed up the POME and CM mixture to maintain the required reactor temperature (35°C). The produced energy was analyzed at 0:100, 25:75, 50:50, 75:25, and 100:0 mixing ratios of POME and CM. The total biogas amount was collected in a gas bag and biogas volume was measured by the water displacement method. The mixture with equal proportions of POME and CM produced the maximum amount of biogas, i.e., 1567.00 mL, while the methane content was 64.13%. The effect of OHP at 1.00% dose with 1 mM FeCl3 addition for Fenton reaction on the POME at 30 min exposure on chemical oxygen demand (COD) and total organic carbon (TOC) removal was 33.80% and 28.31%. The improvement of biodegradable dissolved organic carbon (BDOC) was 59% more for POME at 1.00% OHP doses and thus, BOD/COD was also enhanced up to 0.72 for POME. Biogas and biomethane production can be enhanced up to 46.00% and 64.83% if treated by 1.00% OHP doses. The methane composition is also enhanced up to 72.4% compared to control which was 64.13%. Biogas yield was indicated as the consequence of NH4+ toxicity. To regulate the toxicity impact of the ammonium bicarbonate on the ACoD system, a cycle of dosing from 10 to 40 mg/L was supplemented. The cumulative biogas production of 2034.00 mL was found with the addition of 10 mg/L ammonium bicarbonate and 29.80% more which are higher than that of the control ACoD operation. In ANN, the proposed multi-layered feed-forward neural network model could predict the outcomes of biogas production from the ACoD process with a mean squared error for validation of 0.0562 and an R-value for validation of 0.97733. The approach was found to be effective, flexible and versatile in coping with the non-linear relationships using available information. The economic impact of constructing a biogas plant has been successfully analyzed and predicted as well. The proposed biogas plant seems to be economically feasible because an approximately 3-year payback period, internal rate of return of 23.62% and benefitcost ratio of 1.34 on investment could be achieved if this technology is used on a large scale. So, overall this study may help in minimizing the adverse environmental effects of POME by ACoD treatment in the future and demonstrated that a complete solution to the application of SABr in the integration of different features for enhanced biogas production

An improved data classification framework based on fractional particle swarm optimization

Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high dimensional-search problems with multiple local optima. Therefore, this research proposed an improved Mutually-Optimized Fractional PSO (MOFPSO) algorithm based on fractional derivatives and small step lengths to ensure convergence to global optima by supplying a fine balance between exploration and exploitation. The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. The proposed MOFPSO algorithm demonstrated lowest Mean of Error values during the optimization on all benchmark functions through all 30 runs (Ackley = 0.2, Rosenbrock = 0.2, Bohachevsky = 9.36E-06, Easom = -0.95, Griewank = 0.01, Rastrigin = 2.5E-03, Schaffer = 1.31E-06, Schwefel 1.2 = 3.2E-05, Sphere = 8.36E-03, Step = 0). Furthermore, the proposed MOFPSO algorithm is hybridized with Back-Propagation (BP), Elman Recurrent Neural Networks (RNN) and Levenberg-Marquardt (LM) Artificial Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven existing techniques. It can be concluded from the simulation results that the proposed MOFPSO-ERNN classification algorithm demonstrated good classification performance in terms of classification accuracy (Breast Cancer = 99.01%, EEG = 99.99%, PIMA Indian Diabetes = 99.37%, Iris = 99.6%, Thyroid = 99.88%) as compared to the existing hybrid classification techniques. Hence, the proposed technique can be employed to improve the overall classification accuracy and reduce the computational time in data classification applications

A study of genetic programming and grammatical evolution for automatic object-oriented programming.

Master of Science in Computer Science. University of KwaZulu-Natal, Pietermaritzburg 2016.Manual programming is time consuming and challenging for a complex problem. For efficiency of the manual programming process, human programmers adopt the object-oriented approach to programming. Yet, manual programming is still a tedious task. Recently, interest in automatic software production has grown rapidly due to global software demands and technological advancements. This study forms part of a larger initiative on automatic programming to aid manual programming in order to meet these demands. In artificial intelligence, Genetic Programming (GP) is an evolutionary algorithm which searches a program space for a solution program. A program generated by GP is executed to yield a solution to the problem at hand. Grammatical Evolution (GE) is a variation of genetic programming. GE adopts a genotype-phenotype distinction and maps from a genotypic space to a phenotypic (program) space to produce a program. Whereas the previous work on object-oriented programming and GP has involved taking an analogy from object-oriented programming to improve the scalability of genetic programming, this dissertation aims at evaluating GP and a variation thereof, namely, GE, for automatic object-oriented programming. The first objective is to implement and test the abilities of GP to automatically generate code for object-oriented programming problems. The second objective is to implement and test the abilities of GE to automatically generate code for object-oriented programming problems. The third objective is to compare the performance of GP and GE for automatic object-oriented programming. Object-Oriented Genetic Programming (OOGP), a variation of OOGP, namely, Greedy OOGP (GOOGP), and GE approaches to automatic object-oriented programming were implemented. The approaches were tested to produce code for three object-oriented programming problems. Each of the object-oriented programming problems involves two classes, one with the driver program and the Abstract Data Type (ADT) class. The results show that both GP and GE can be used for automatic object-oriented programming. However, it was found that the ability of each of the approaches to automatically generate code for object-oriented programming problems decreases with an increase in the problem complexity. The performance of the approaches were compared and statistically tested to determine the effectiveness of each approach. The results show that GE performs better than GOOGP and OOGP

Energy and performance-optimized scheduling of tasks in distributed cloud and edge computing systems

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival and heterogeneity of tasks. This dissertation proposes a class of energy and performance-optimized scheduling algorithms built on top of several intelligent optimization algorithms. This dissertation includes two parts, including background work, i.e., Chapters 3–6, and new contributions, i.e., Chapters 7–11. 1) Background work of this dissertation. Chapter 3 proposes a spatial task scheduling and resource optimization method to minimize the total cost of CDCs where bandwidth prices of Internet service providers, power grid prices, and renewable energy all vary with locations. Chapter 4 presents a geography-aware task scheduling approach by considering spatial variations in CDCs to maximize the profit of their providers by intelligently scheduling tasks. Chapter 5 presents a spatio-temporal task scheduling algorithm to minimize energy cost by scheduling heterogeneous tasks among CDCs while meeting their delay constraints. Chapter 6 gives a temporal scheduling algorithm considering temporal variations of revenue, electricity prices, green energy and prices of public clouds. 2) Contributions of this dissertation. Chapter 7 proposes a multi-objective optimization method for CDCs to maximize their profit, and minimize the average loss possibility of tasks by determining task allocation among Internet service providers, and task service rates of each CDC. A simulated annealing-based bi-objective differential evolution algorithm is proposed to obtain an approximate Pareto optimal set. A knee solution is selected to schedule tasks in a high-profit and high-quality-of-service way. Chapter 8 formulates a bi-objective constrained optimization problem, and designs a novel optimization method to cope with energy cost reduction and QoS improvement. It jointly minimizes both energy cost of CDCs, and average response time of all tasks by intelligently allocating tasks among CDCs and changing task service rate of each CDC. Chapter 9 formulates a constrained bi-objective optimization problem for joint optimization of revenue and energy cost of CDCs. It is solved with an improved multi-objective evolutionary algorithm based on decomposition. It determines a high-quality trade-off between revenue maximization and energy cost minimization by considering CDCs’ spatial differences in energy cost while meeting tasks’ delay constraints. Chapter 10 proposes a simulated annealing-based bees algorithm to find a close-to-optimal solution. Then, a fine-grained spatial task scheduling algorithm is designed to minimize energy cost of CDCs by allocating tasks among multiple green clouds, and specifies running speeds of their servers. Chapter 11 proposes a profit-maximized collaborative computation offloading and resource allocation algorithm to maximize the profit of systems and guarantee that response time limits of tasks are met in cloud-edge computing systems. A single-objective constrained optimization problem is solved by a proposed simulated annealing-based migrating birds optimization. This dissertation evaluates these algorithms, models and software with real-life data and proves that they improve scheduling precision and cost-effectiveness of distributed cloud and edge computing systems