Applying Genetic Algorithm and Neural Network to the Conjunctive Use of Surface and Subsurface Water

The conjunctive use of surface and subsurface water is one of the most effective ways to increase water supply reliability with minimal cost and environmental impact. This study presents a novel stepwise optimization model for optimizing the conjunctive use of surface and subsurface water resource management. At each time step, the proposed model decomposes the nonlinear conjunctive use problem into a linear surface water allocation sub-problem and a nonlinear groundwater simulation sub-problem. Instead of using a nonlinear algorithm to solve the entire problem, this decomposition approach integrates a linear algorithm with greater computational efficiency. Specifically, this study proposes a hybrid approach consisting of Genetic Algorithm (GA), Artificial Neural Network (ANN), and Linear Programming (LP) to solve the decomposed two-level problem. The top level uses GA to determine the optimal pumping rates and link the lower level sub-problem, while LP determines the optimal surface water allocation, and ANN performs the groundwater simulation. Because the optimization computation requires many groundwater simulations, the ANN instead of traditional numerical simulation greatly reduces the computational burden. The high computing performance of both LP and ANN significantly increase the computational efficiency of entire model. This study examines four case studies to determine the supply efficiencies under different operation models. Unlike the high interaction between climate conditions and surface water resource, groundwater resources are more stable than the surface water resources for water supply. First, results indicate that adding an groundwater system whose supply productivity is just 8.67 % of the entire water requirement with a surface water supply first (SWSF) policy can significantly decrease the shortage index (SI) from 2.93 to 1.54. Second, the proposed model provides a more efficient conjunctive use policy than the SWSF policy, achieving further decrease from 1.54 to 1.13 or 0.79, depending on the groundwater rule curves. Finally, because of the usage of the hybrid framework, GA, LP, and ANN, the computational efficiency of proposed model is higher than other models with a purebred architecture or traditional groundwater numerical simulations. Therefore, the proposed model can be used to solve complicated large field problems. The proposed model is a valuable tool for conjunctive use operation planning.     

Strategic environmental assessment for energy production

Amongst the approaches that have developed to improve environmental protection within the energy sector, strategic environmental assessment (SEA) has received relatively little attention. This is despite its potential to overcome some of the shortcomings associated with project-level assessment by intervening at higher levels of energy system planning. In this article, a review is presented of the extent to which SEA has been adopted and otherwise promoted in strategic energy planning processes in a wide range of countries throughout the world (with an emphasis on European Union nations). In this regard, the growing importance of regulatory compliance is underlined, especially within the EU, with a particular focus upon the application of SEA to grid systems. The case of the Belgian transmission system is described, illustrating a proactive approach to SEA. But the difficulties inherent in introducing SEA to an increasingly fragmented and liberalised sector are also drawn out, leading to suggestions by which these difficulties may be addressed.     

Haptic feedback and control of a flexible surgical endoscopic robot

A flexible endoscope could reach the potential surgical site via a single small incision on the patient or even through natural orifices, making it a very promising platform for surgical procedures. However, endoscopic surgery has strict spatial constraints on both tool-channel size and surgical site volume. It is therefore very challenging to deploy and control dexterous robotic instruments to conduct surgical procedures endoscopically. Pioneering endoscopic surgical robots have already been introduced, but the performance is limited by the flexible neck of the robot that passes through the endoscope tool channel. In this article we present a series of new developments to improve the performance of the robot: a force transmission model to address flexibility, elongation study for precise position control, and tissue property modeling for haptic feedback. Validation experiment results are presented for each sector. An integrated control architecture of the robot system is given in the end.     

Construction application of Fibre/Mesh method for protecting concrete columns in fire

In this paper, a construction application of a technique to improve the fire resistance of high strength concrete is presented. Before the construction application, fire tests of three hours duration were carried out in accordance with ISO-834. Three types of mitigating methods, namely, a specimen with no protection (control), with fireproof panel protection and with the combination of polypropylene fibre reinforcement and metal mesh confinement (Fibre/Mesh), were applied to full sized columns for comparison purposes. The fire resistance performance of all columns was tested under loads. After the fire tests, the severity of spalling of specimens was assessed, and the best performing technique, Fibre/Mesh, for spalling protection was selected for an application in practice. The process of the construction application is described in detail, including pumping, pouring and compacting of the concrete. The concrete quality was examined and the behaviour of the concrete was observed. Consequently, it was verified that this new spalling resistance technique is feasible in practice.     

An online adaptive condition-based maintenance method for mechanical systems

This paper proposes an online adaptive condition-based maintenance method with pattern discovery and fault learning capabilities for mechanical systems. The method is mainly based on a subtype of neural network techniques called self-organizing map (SOM). It is able to reduce local clusters from the same pattern and optimize the SOM architecture to further decrease the calculation cost in matching patterns in the neuron fitting process. Moreover, distance analysis and statistical pattern recognition (SPR) on neurons of the SOM are combined to establish rules and criteria for conducting and controlling the discovery and learning process so continuous process as purging prototypes on the map can be avoided. An experiment on condition monitoring of a machine tool test bed demonstrates and validates the effectiveness of the proposed approach.     

Modulation of Viscoelasticity and HIV Transport as a Function of pH in a Reversibly Crosslinked Hydrogel

Materials that respond to physiological stimuli are important in developing advanced biomaterials for modern therapies. The reversibility of covalent crosslinks formed by phenylboronate (PBA) and salicylhydroxamate (SHA) has been exploited to provide a pH‐responsive gel for application to the vaginal tract. Dynamic rheology reveals that the gel frequency‐dependent viscoelastic properties are modulated by pH. At pH 4.8 the viscous component dominates throughout most of the frequency range. As the pH increases, the characteristic relaxation time continues to increase while the G′_Plateau levels off above pH 6. At pH 7.5, the elastic component dominates throughout the frequency sweep and is predominately independent of frequency. Particle tracking assesses the transport of both fluorescently labeled HIV‐1 and 100‐nm latex particles in the PBA–SHA crosslinked gel as a function of pH. At pH 4.8 the ensemble‐averaged mean squared displacement at lag times greater than three seconds reveals that transport of the HIV‐1 and 100‐nm particles becomes significantly impeded by the matrix, exhibiting diffusion coefficients less than 0.0002 µm² s^?1. This pH‐responsive gel thus displays properties that have the potential to significantly reduce the transport of HIV‐1 to susceptible tissues and thus prevent the first stage of male‐to‐female transmission of HIV‐1.     

Quantification of margins and uncertainties: Example analyses from reactor safety and radioactive waste disposal involving the separation of aleatory and epistemic uncertainty

In 2001, the National Nuclear Security Administration (NNSA) of the U.S. Department of Energy (DOE) in conjunction with the national security laboratories (i.e., Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Sandia National Laboratories) initiated development of a process designated quantification of margins and uncertainties (QMU) for the use of risk assessment methodologies in the certification of the reliability and safety of the nation's nuclear weapons stockpile. A previous presentation, “Quantification of Margins and Uncertainties: Conceptual and Computational Basis,” describes the basic ideas that underlie QMU and illustrates these ideas with two notional examples. The basic ideas and challenges that underlie NNSA's mandate for QMU are present, and have been successfully addressed, in a number of past analyses for complex systems. To provide perspective on the implementation of a requirement for QMU in the analysis of a complex system, three past analyses are presented as examples: (i) the probabilistic risk assessment carried out for the Surry Nuclear Power Station as part of the U.S. Nuclear Regulatory Commission's (NRC's) reassessment of the risk from commercial nuclear power in the United States (i.e., the NUREG-1150 study), (ii) the performance assessment for the Waste Isolation Pilot Plant carried out by the DOE in support of a successful compliance certification application to the U.S. Environmental Agency, and (iii) the performance assessment for the proposed high-level radioactive waste repository at Yucca Mountain, Nevada, carried out by the DOE in support of a license application to the NRC. Each of the preceding analyses involved a detailed treatment of uncertainty and produced results used to establish compliance with specific numerical requirements on the performance of the system under study. As a result, these studies illustrate the determination of both margins and the uncertainty in margins in real analyses.     

National and regional generation of municipal residue biomass and the future potential for waste-to-energy implementation

Municipal residue biomass (MRB) in the municipal solid waste (MSW) stream is a potential year-round bioenergy feedstock. A method is developed to estimate the amount of residue biomass generated by the end-user at the scale of a country using a throughput approach. Given the trade balance of food and forestry products, the amount of MRB generated is calculated by estimating product lifetimes, discard rates, rates of access to MSW collection services, and biomass recovery rates. A wet tonne of MRB could be converted into about 8 GJ of energy and 640 kg of carbon dioxide (CO₂) emissions, or buried in a landfill where it would decompose into 1800 kg of CO₂ equivalent (in terms of global warming potential) methane (CH₄) and CO₂ emissions. It is estimated that approximately 1.5 Gt y^?1 of MRB are currently collected worldwide. The energy content of this biomass is approximately 12 EJ, but only a fraction is currently utilized. An integrated assessment model is used to project future MRB generation and its utilization for energy, with and without a hypothetical climate policy to stabilize atmospheric CO₂ concentrations. Given an anticipated price for biomass energy (and carbon under a policy scenario), by the end of the century, it is projected that nearly 60% of global MRB would be converted to about 8 EJ y^?1 of energy in a reference scenario, and nearly all of global MRB would be converted into 16 EJ y^?1 of energy by the end of the century under a climate policy scenario.