Uncertainty Analyis In Modeling

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

Journal of Materials in Civil Engineering

Abstract PDF
Nnadi, E. and Lizarazo-Marriaga, J. Acid Corrosion of Plain and Reinforced Concrete Sewage Systems. J. Mater. Civ. Eng.. .
doi: 10.1061/(ASCE)MT.1943-5533.0000641
Acid Corrosion of Plain and Reinforced Concrete Sewage Systems

ARTICLE HISTORY
Submitted: 27 September 2011
Accepted: 27 July 2012
Published: 25 August 2012
PUBLICATION DATA
ISSN (print): 0899-1561
ISSN (online): 1943-5533
Publisher: American Society of Civil Engineers

Ernest O. Nnadi, Ph.D., M.ASCE:
Coventry University, Coventry CV1 5FB — United Kingdom
Juan Lizarazo-Marriaga, Ph.D.:
Grupo de Investigación en Estructuras y Materiales — GIES, Universidad Nacional de Colombia, Bogotá

Abstract
The infrastructure of concrete sewage systems is affected notably by biogenic sulfuric acid attack, which causes rapid deterioration of each of its components. In order to provide a better understanding of this mechanism of damage, this paper reports on research carried out to study the response of the cementitious concrete matrix and the reinforcement steel under the attack of sulfuric acid. Concrete industrial pipes were cast using ordinary Portland cement and pulverized fly ash and specimens obtained from the mixes were left several cycles in solution of sulfuric acid at different pH. Experiments were carried out to assess the loss of weight and the electrical corrosion potential. Results showed that sulfuric acid attack can be considered a surface phenomenon, in which acid attack destroys the cover of concrete before to reach the reinforcement.
Permalink: http://dx.doi.org/10.1061/(ASCE)MT.1943-5533.0000641
© 2012 American Society of Civil Engineers

Existing compartmental models in epidemiology are limited in terms of optimizing the resource allocation to control an epidemic outbreak under disease growth uncertainty. In this study, we address this core limitation by presenting a multi-stage stochastic programming compartmental model, which integrates the uncertain disease progression and resource allocation to control an infectious disease outbreak. The proposed multi-stage stochastic program involves various disease growth scenarios and optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals. We define two new equity metrics, namely infection and capacity equity, and explicitly consider equity for allocating treatment funds and facilities over multiple time stages. We also study the multi-stage value of the stochastic solution (VSS), which demonstrates the superiority of the proposed stochastic programming model over its deterministic counterpart. We apply the proposed formulation to control the Ebola Virus Disease (EVD) in Guinea, Sierra Leone, and Liberia of West Africa to determine the optimal and fair resource-allocation strategies. Our model balances the proportion of infections over all regions, even without including the infection equity or prevalence equity constraints. Model results also show that allocating treatment resources proportional to population is sub-optimal, and enforcing such a resource allocation policy might adversely impact the total number of infections and deaths, and thus resulting in a high cost that we have to pay for the fairness. Our multi-stage stochastic epidemic-logistics model is practical and can be adapted to control other infectious diseases in meta-populations and dynamically evolving situations.