Informational Entropy: a Failure Tolerance and Reliability Surrogate for Water Distribution Networks

Evolutionary algorithms are used widely in optimization studies on water distribution networks. The optimization algorithms use simulation models that analyse the networks under various operating conditions. The solution process typically involves cost minimization along with reliability constraints that ensure reasonably satisfactory performance under abnormal operating conditions also. Flow entropy has been employed previously as a surrogate reliability measure. While a body of work exists for a single operating condition under steady state conditions, the effectiveness of flow entropy for systems with multiple operating conditions has received very little attention. This paper describes a multi-objective genetic algorithm that maximizes the flow entropy under multiple operating conditions for any given network. The new methodology proposed is consistent with the maximum entropy formalism that requires active consideration of all the relevant information. Furthermore, an alternative but equivalent flow entropy model that emphasizes the relative uniformity of the nodal demands is described. The flow entropy of water distribution networks under multiple operating conditions is discussed with reference to the joint entropy of multiple probability spaces, which provides the theoretical foundation for the optimization methodology proposed. Besides the rationale, results are included that show that the most robust or failure-tolerant solutions are achieved by maximizing the sum of the entropies.     

Optimization of Industrial Structure Considering the Uncertainty of Water Resources

This paper developed a stochastic linear fractional programming model for industry optimization allocation base on the uncertainty of water resources incorporating chance constrained programming and fractional programming. In this paper, the stochastic linear fractional programming is used in the real word. The development SLFP has the following advantages: (1) The model can compare the two aspects of the targets; (2) The model can reflect the system efficiency intuitively; (3) The model can deal with uncertain issues with probability distribution; (4) The model can give different optimal plans under different risk conditions. The model has a significant value for the industry optimization allocation under uncertainty in local and areas to achieve the maximum economic benefits and the full use of the water resources.     

The usability of digestate in organic farming

As organic farming prohibits the use of synthetic fertilisers, animal slurries and manures must be used. Digestate offers an alternative to these and this study reports on three experiments conducted to determine its usability in terms of: (1) the effect on earthworm populations, (2) its fertilising effects on Italian Ryegrass and wild Creeping Thistle, and (3) the suppression effects digestate has on weed emergence. The results for digestate application to field plots were intermediate between slurry and no treatment for earthworm attraction and wild thistle suppression. In glasshouse trials it led to increased ryegrass growth compared with undigested slurry. Analysis showed that the digestate had improved nitrogen availability, leading to increased plant growth, but a reduced organic matter content compared with the slurry, leading to a positive though less beneficial impact on the earthworms. Digestate therefore provides a suitable fertiliser for organic farming. This suitability could be improved by drying or separation to increase the OM content making its properties closer to those of slurry whilst still retaining the higher content of plant available nitrogen.     

Artificial Life Algorithm for Management of Multi-reservoir River Systems

The design and operation of civil engineering systems, particularly water resources systems, has been pursued from the perspective of minimizing costs and related negative impacts, maximizing benefits, or a combination thereof. Due to the complex, nonlinear nature of the majority of systems, together with an increase in digital computing capabilities, global search algorithms are becoming a common means of meeting these objectives. This paper employs an artificial life algorithm, derived from the artificial life paradigm. The algorithm is evaluated using standard optimization test functions and is subsequently applied to determine optimal dam operations in multi-reservoir river systems. The optimal dam operation scheme is that which indirectly minimizes environmental impacts caused by short-term water level fluctuations. Optimal releases are sought by coupling an artificial life algorithm with FLDWAV, a one-dimensional, steady flow simulation model. The resulting multi-reservoir management model is successfully applied to a portion of the Illinois River Waterway.     

The use of NDVI and its Derivatives for Monitoring Lake Victoria’s Water Level and Drought Conditions

Normalized Difference Vegetation Index (NDVI), which is a measure of vegetation vigour, and lake water levels respond variably to precipitation and its deficiency. For a given lake catchment, NDVI may have the ability to depict localized natural variability in water levels in response to weather patterns. This information may be used to decipher natural from unnatural variations of a given lake’s surface. This study evaluates the potential of using NDVI and its associated derivatives (VCI (vegetation condition index), SVI (standardised vegetation index), AINDVI (annually integrated NDVI), green vegetation function (F _g ), and NDVIA (NDVI anomaly)) to depict Lake Victoria’s water levels. Thirty years of monthly mean water levels and a portion of the Global Inventory Modelling and Mapping Studies (GIMMS) AVHRR (Advanced Very High Resolution Radiometer) NDVI datasets were used. Their aggregate data structures and temporal co-variabilities were analysed using GIS/spatial analysis tools. Locally, NDVI was found to be more sensitive to drought (i.e., responded more strongly to reduced precipitation) than to water levels. It showed a good ability to depict water levels one-month in advance, especially in moderate to low precipitation years. SVI and SWL (standardized water levels) used in association with AINDVI and AMWLA (annual mean water levels anomaly) readily identified high precipitation years, which are also when NDVI has a low ability to depict water levels. NDVI also appears to be able to highlight unnatural variations in water levels. We propose an iterative approach for the better use of NDVI, which may be useful in developing an early warning mechanisms for the management of lake Victoria and other Lakes with similar characteristics.     

Disruption of Synechocystis PCC 6803 for lipid extraction

In order to extract intracellular lipids from cyanobacterial Synechocystis PCC 6803 for biofuel production, seven cell-disruption methods - autoclaving, bead beating, freeze drying, French press, microwave, pulsed electric fields (PEF), and ultrasound - were tested prior to lipid extraction to make intracellular lipids more accessible by organic solvents. The different methods brought about distinct disruption effects to the cell envelope, plasma membrane, and thylakoid membranes that were related to extraction efficiency. Microwave, PEF, and ultrasound with temperature control had significant enhancement of lipid extraction (9-13% increases). Bead beating, freeze drying, and French press did not provide significant enhancement of lipid extraction. Furthermore, autoclaving, French press, and ultrasound treatments caused significant release of lipid into the medium, which may increase solvent usage and make medium recycling difficult. In order to minimize the cost of cell-disruption and lipid-extraction steps, microwave and PEF (with temperature control) might be best suited for large-scale cell disruption among all techniques investigated.     

A GIS Framework for Changing Cropping Pattern Under Different Climate Conditions and Irrigation Availability Scenarios

Irrigation water availability is a main driver which determines cropping patterns for an irrigation area. Irrigation water availability will potentially reduce due to changes in climate and irrigation extraction limits. Cropping patterns should be adjusted to meet this challenge. This study presents a new approach for assessing future cropping patterns using GIS in combination with an Irrigation Water Availability Simulation model (IWAS) at irrigation area scale. The IWAS-GIS framework was developed for analysis of cropping pattern options based on the forecast of irrigation water availability in 2030 for the Murrumbidgee Irrigation Area (MIA) which is one of the most important irrigation areas in Australia. Six scenarios considering climatic conditions and the irrigation availabilities were input into the IWAS-GIS framework to simulate cropping pattern changes corresponding to predicted monthly irrigation water availability. Cropping patterns were designed by integration of soil type and irrigation water availability to improve irrigation sustainability. Simulations results indicate that the total irrigated areas will likely decrease with drier climate and less irrigation availability. Rice and pasture areas change significantly when climate and irrigation availability varies. Under the same climate condition, horticulture area increases when irrigation availability decrease. The percentage of lands which are unsuitable for cropping has reduced with the drying climate and shrinking irrigation availability. The IWAS-GIS is simple to implement and provides an easy way for assessing spatial cropping pattern changes based on irrigation water availability. This framework was developed to help irrigators plan cropping patterns corresponding to irrigation water availability. It is flexible to be adopted for similar applications in other irrigation areas.     

The Electronic Encapsulation of Knowledge for Groundwater Quality Management

Computers have become the main tool used in Ground Water Quality Management (GWQM). Computer software has been developed to encapsulate, process and present information on groundwater pollution problems. Continuing demands for more efficient handling of information have resulted in increasing integration of the software into Decision Support Systems (DSSs). Encapsulation and integration of both quantitative (numerical) and qualitative (knowledge) information are required to support decision-making processes. A review of current achievements in the software integration and especially in knowledge encapsulation reveals no clear developing directions (research lines) in these fields. Attention must be therefore directed to: (1) the main GWQM tasks, seen in the light of knowledge encapsulation, and (2) postulates and techniques of Artificial Intelligence (AI) related to knowledge encapsulation and DSS development. The case-specific nature of groundwater pollution problems and a lack of formalised expertise in GWQM are the main difficulties in development of knowledge-containing systems. Work on the taxonomy of GWQM tasks and related knowledge is recommended to enable broader application of AI in knowledge encapsulation, as well as in development of DSSs for GWQM as a whole.