Standardization of the Electricity and Economic Potentials of Landfill Gas (LFG) in Lagos, Nigeria.

Globally, various practical data and scholarly estimations of the electricity potentials of landfill gas (LFG) have been forwarded and these can be juxtaposed for estimations in the megacity called Lagos. The calculated values were between 63.22- 700MW of derivable electricity. However, in order to limit observable disparities and ambiguities in these derivations and thus allow for more accurate projections, these estimations can be gauged using as template; -stoichiometry, establishing 50% of landfill gas as methane, assuming 50% of this volume as recoverable, and using a proposed engine efficiency of 30%. This standardization projects a theoretical mean achievable electrical power of 121.69 MW for the Lagos area from a population of about 21 million with a generation per capita (GPC) of 0.63kg with biodegradable content of about 60%. The yearly electrical energy was placed at 1,066,004.4 MWh with tariff revenue in excess of US$106.6 million /yr. An accruing carbon credit of about US$75.59 million /yr is expected from certified emission reduction (CER). The projected derivations can be used as models for evaluation of the landfill gas and electricity potentials in many parts of the world

A new design equation for prediction of ultimate bearing capacity of shallow foundation on granular soils

A major concern in design of structures is to provide precise estimations of ultimate bearing capacity of soil beneath their foundations. Direct determination of the bearing capacity of foundations requires performing expensive and time consuming laboratory tests. To cope with this issue, several numerical models have been presented by researchers. This paper presents the development of a new design equation for the prediction of the ultimate bearing capacity of shallow foundations on granular soils using linear genetic programming (LGP) methodology. The ultimate bearing capacity is formulated in terms of width of footing, footing geometry, depth of footing, unit weight of sand, and angle of shearing resistance. The LGP-based design equation is established using the results of several load tests on real sized foundations presented in the literature. Validity of the model is verified using a part of laboratory data that are not involved in the calibration process. The statistical measures of coefficient of determination, root mean squared error and mean absolute error are used to evaluate the performance of the model. Sensitivity and parametric analyses are conducted and discussed. The proposed model accurately characterizes the ultimate bearing capacity resulting in a very good prediction performance. The LGP model reaches a better prediction performance than the well-known prediction equations for the bearing capacity of shallow foundations

Optimization and control of a thin film growth process: A hybrid first principles/artificial neural network based multiscale modelling approach

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.compchemeng.2018.08.029 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/This work details the construction and evaluation of a low computational cost hybrid multiscale thin film deposition model that couples artificial neural networks (ANNs) with a mechanistic (first-principles) multiscale model. The multiscale model combines continuum differential equations, which describe the transport of the precursor gas phase, with a stochastic partial differential equation (SPDE) that predicts the evolution of the thin film surface. In order to allow the SPDE to accurately predict the thin film growth over a range of system parameters, an ANN is developed and trained to predict the values of the SPDE coefficients. The fully-assembled hybrid multiscale model is validated through comparison against a kinetic Monte Carlo-based thin film multiscale model. The model is subsequently applied to a series of optimization and control studies to test its performance under different scenarios. These studies illustrate the computational efficiency of the proposed hybrid multiscale model for optimization and control applications.Natural Sciences and Engineering Research Council of Canad

Estudo de caso para geração de Energia Elétrica a partir de Biogás de Resíduos Sólidos para o município de Itanhandu - MG.

O presente trabalho de dissertação contém um estudo de caso para o município de Itanhandu – MG, caracterizado por sua população pequena e grande atividade rural, onde são propostos três cenários de aproveitamento energético para geração elétrica a partir de biogás de resíduos sólidos. Em um primeiro cenário, é analisada a viabilidade de um empreendimento gerando energia elétrica a partir de biogás dos resíduos sólidos urbanos de um aterro proposto para o município. No segundo cenário o aproveitamento energético é realizado a partir do biogás de resíduos sólidos rurais (fezes de galinhas). Por fim, a terceira análise dá-se pela geração de energia elétrica a partir do biogás de ambas as fontes anteriores. Os resultados encontrados evidenciam a inviabilidade econômica do empreendimento baseado somente no aterro sanitário, enquanto nos outros dois cenários os resultados são positivos, mostrando-se viáveis sob as perspectivas propostas e analisadas. O comportamento da primeira análise está de acordo com resultados encontrados na literatura para viabilidade de tais aproveitamentos em municípios pequenos, com relação ao número de habitantes, reforçando os resultados obtidos

Modelling and Optimization of a Pilot-Scale Entrained Flow gasifier using Artificial Neural Networks

In this research, the construction and validation of both ANN and RNN models was presented to accurately and efficiently predict both steady state and dynamic performance of a pilot-scale gasifier unit. The corresponding ANN and RNN models’ performance were validated using data generated from a gasifier’s ROM. After validation of ANN and RNN models, optimization studies on the steady state and transient performance of the gasifier were performed under different scenarios. In the optimization studies at steady state, results show that increasing the peak temperature limitation of the gasifier can promote a high maximum carbon conversion. In the dynamic optimization studies, the results show that increasing the peak temperature limitation of the gasifier can lead to higher CO compositions at the outlet of the gasifier. These optimization studies further showcase the benefit of the ANN and RNN models, which were able to obtain relatively accurate predictions for the gasifier similar to the results generated by ROM at a much lower computational cost

Simulation, optimization and instrumentation of agricultural biogas plants

During the last two decades, the production of renewable energy by anaerobic digestion (AD) in biogas plants has become increasingly popular due to its applicability to a great variety of organic material from energy crops and animal waste to the organic fraction of Municipal Solid Waste (MSW), and to the relative simplicity of AD plant designs. Thus, a whole new biogas market emerged in Europe, which is strongly supported by European and national funding and remuneration schemes. Nevertheless, stable and efficient operation and control of biogas plants can be challenging, due to the high complexity of the biochemical AD process, varying substrate quality and a lack of reliable online instrumentation. In addition, governmental support for biogas plants will decrease in the long run and the substrate market will become highly competitive. The principal aim of the research presented in this thesis is to achieve a substantial improvement in the operation of biogas plants. At first, a methodology for substrate inflow optimization of full-scale biogas plants is developed based on commonly measured process variables and using dynamic simulation models as well as computational intelligence (CI) methods. This methodology which is appliquable to a broad range of different biogas plants is then followed by an evaluation of existing online instrumentation for biogas plants and the development of a novel UV/vis spectroscopic online measurement system for volatile fatty acids. This new measurement system, which uses powerful machine learning techniques, provides a substantial improvement in online process monitoring for biogas plants. The methodologies developed and results achieved in the areas of simulation and optimization were validated at a full-scale agricultural biogas plant showing that global optimization of the substrate inflow based on dynamic simulation models is able to improve the yearly profit of a biogas plant by up to 70%. Furthermore, the validation of the newly developed online measurement for VFA concentration at an industrial biogas plant showed that a measurement accuracy of 88% is possible using UV/vis spectroscopic probes