Assessment of Urban Water Supply System Based on Query Optimization Strategy

As a complicated water treatment system project, an urban water supply plays a significant role in enhancing ecological civilization construction, promoting social-economic sustainable development, and improving the living environment of humans. This paper has the goal of improving water treatment efficiency and reducing water treatment cost based on comparative studies by applying two types of distributed database query optimization methods, including the system for a distributed database (SDD-1) and all reduction algorithms. The approach involves the components of partial relations and deletes the database components that are unrelated to a query, using only attributes related to a query. Thereby, the realization process becomes simple, with a small amount of transmitted data. The results show that the validity and convenience of all reduction algorithms involve the whole query process. An algorithm is independent of the chart of static characteristics and can realize all reduction states without depending on analyzing the benefits from intermediate semijoin results, which will ultimately contribute to reductions in the transmission of useless data in the network and communication costs. Implementation of the query optimization strategy can improve water treatment efficiency, reduce water treatment cost, lower water treatment expense, and implement effective communication.

Sediment Yield by Category

The land use greatly influence water quality on the one supplied by watersheds. Land use changes often increase significantly the sedimentation and nutrient pollution. Human activity can generate as well pollutant concentration rising and heavily affect water treatment cost for urban supply. This map shows the total quantity of sediment moving out of a watershed in a given time interval, being expressed as units of tonnes/km2/year, organized into four categories: “Low” if less than 6 tonnes/km2; “Medium” if between 6-40 tonnes/km2; “High” if more than 40 tonnes/km2, and “No information”. For more information, access the Urban Water Blueprint report here: http://www.iwa-network.org/wp-content/uploads/2016/06/Urban-Water-Blueprint-Report.pdf You can also visit the Urban Water Blueprint website here: http://water.nature.org/waterblueprint/#/intro=true Basin Drinking Water Groundwater Human Settlements Supply Surface water

Study Case - Low Concentration Fluoride Wastewater from Photovoltaic Enterprises Recycled

A photovoltaic cell company located in China used with double pass reverse osmosis method to treat low concentration fluoride wastewater. Product water entered the pure water preparation system to recycle. The design capacity of wastewater was 180m3/h. The project improved the pretreatment methods, the operating conditions and the cleaning methods of membrane, so as to prevent membrane blocked and increase of service life. This project ran stability.The water treatment cost was about 4 yuan/t, lower than the local water price.

The Observation of Rainwater Harvesting Potential in Mahasarakham University (Khamriang Campus)

Rainwater harvesting from roof is considered as valuable water resources. Material Flow Analysis (MFA) of water in Mahasarakham University (Khamriang Campus) shows that rainwater harvesting from roof can reduce water supply production by 7% and save more than 200,000 Bt/year for water treatment cost. The sensitivity analysis suggests that by 5% water supply conservation and 20% additional rainwater harvesting, MSU could have enough water resources. The rainwater is suitable to be substituted water for gardening due to the convenience to assemble an above ground storage tank or a pond to store harvested rainwater from roof. The current practice of rainwater is collected and discharged into drainage system and treated in wastewater treatment plant. Utilisation of rainwater harvested could reduce wastewater amount that must be treated by 9%. Rainwater harvesting and reuse should be promoted in campus in order to encourage sustainable living and water conservation policy.

Optimisation and economical evaluation of infill drilling in CSG reservoirs using a multi-objective genetic algorithm

Water production in the early life of Coal Seam Gas (CSG) recovery makes these reservoirs different from conventional gas reservoirs. Normally, a large amount of water is produced during the early production period, while the gas-rate is negligible. It is essential to drill infill wells in optimum locations to reduce the water production and increase the gas recovery. To optimise infill locations in a CSG reservoir, an integrated framework is developed to couple the reservoir flow simulator (ECLIPSE) and the genetic algorithm (GA) optimisation toolbox of (MATLAB). In this study, the desired objective function is the NPV of the infill drilling. To obtain the economics of the infill drilling project, the objective function is split into two objectives. The first objective is the gas income; the second objective is the cost associated with water production. The optimisation problem is then solved using the multi-objective solver. The economics of the infill drilling program is investigated for a case study constructed based on the available data from the Tiffany unit in San Juan basin when gas price and water treatment cost are variable. Best obtained optimal locations of 20 new wells in the reservoir are attained using this optimisation framework to maximise the profit of this project. The results indicate that when the gas price is less than $2/Mscf, the infill plan, regardless of the cost of water treatment, is not economical and drilling additional wells cannot be economically justified. When the cost of water treatment and disposal increases from $0.01/STB to $4/STB, the optimisation framework intelligently distributes the infill wells across the reservoir in a way that the total water production of infill wells is reduced by 26%. Simulation results also indicate that when water treatment is an expensive operation, lower water production is attained by placing the infill wells in depleted sections of the coal bed, close to the existing wells. When water treatment cost is low, however, infill wells are freely allocated in virgin sections of the coal bed, where both coal gas content and reservoir pressure are high.