Applying Knowledge Management to Analyze Buyer's Trend

The main objective for this project is to develop an intelligent website which can help to analyze buyer's trend, and extract knowledge from dataset within a database. This website analysis could actually help us in discovering knowledge and extract the meaning of the data. It allows us to make proactive and knowledge-driven decision. It can answer business questions that traditionally were too time consuming to resolve. This research is on knowledge management in e-comrnerce, it is used to discover patterns and relationships in the data in order to help make better business decision. Compared to the manual way, it is impossible to process the whole data in order to extract the meaning of the data. Since it need a lot of time to do such thing and it can cause mistakes and errors of data, besides it is impossible to manage them manually. For this project, an e-comrnerce website will be developed to manage the testing of the data analysis. From the input from the e-commerce website, the website could be able to extract the patterns and trends of the customers and suggested any decisions based on the analyzed result. But from my research, the scope for this area is too wide and I need to stress it to the least scope. The method that is going to be applied in this project is the waterfall model

Applying Knowledge Management to Analyze Buyer's Trend

The main objective for this project is to develop an intelligent website which can help to analyze buyer's trend, and extract knowledge from dataset within a database. This website analysis could actually help us in discovering knowledge and extract the meaning of the data. It allows us to make proactive and knowledge-driven decision. It can answer business questions that traditionally were too time consuming to resolve. This research is on knowledge management in e-comrnerce, it is used to discover patterns and relationships in the data in order to help make better business decision. Compared to the manual way, it is impossible to process the whole data in order to extract the meaning of the data. Since it need a lot of time to do such thing and it can cause mistakes and errors of data, besides it is impossible to manage them manually. For this project, an e-comrnerce website will be developed to manage the testing of the data analysis. From the input from the e-commerce website, the website could be able to extract the patterns and trends of the customers and suggested any decisions based on the analyzed result. But from my research, the scope for this area is too wide and I need to stress it to the least scope. The method that is going to be applied in this project is the waterfall model

Organic rankine cycle and steam turbine for intermediate temperature waste heat recovery in total site integration

The utilization of waste heat for heat recovery technologies in process sites has been widely known in improving the site energy saving and energy efficiency. The Total Site Heat Integration (TSHI) methodologies have been established over time to assist the integration of heat recovery technologies in process sites with a centralized utility system, which is also known as Total Site (TS). One of the earliest application of TSHI concept in waste heat recovery was through steam turbine using the popular Willan’s line approximation. The TSHI methodologies later were extended to integrate with wide range of heat recovery technologies in many literatures, whereby Organic Rankine Cycle (ORC) has been reported to be the one of the beneficial options for heat recovery. In general, the medium to high temperature waste heat is recovered via condensing/backpressure steam turbine, whereas ORC is targeted for recovering the low temperature waste heat. However, it is known that condensing turbine is also abled to generate power by condensing low grade steam to sub-ambient pressure, which is comparable with ORC integration. In this work, the integration of ORC and condensing turbine was considered for a multiple-process system to recover intermediate temperature waste heat through utility system. This study presented a numerical methodology to investigate the performance analysis of integration of ORC and condensing turbine in process sites for recovering waste heat from a centralized utility system. A modified retrofit case study was used to demonstrate the effectiveness application of the proposed methodology. The performances of ORC and condensing steam turbine were evaluated with the plant total utility costing as the objective function. The turbine integration was found to be more beneficial in the modified case study with lower utility cost involved. However, the capital cost has not been considered in the analysis

Direct and indirect integration of organic rankine cycle in total site

Industrial sector is one of the major energy consumers in the world. Energy inefficiency in industry due to energy losses and wastages, which contributes to unnecessary carbon emission and global warming. Various initiatives have been taken for enhancing the energy efficiency in the industrial sector. Pinch Analysis is one of the important systematic tools for improving energy supply and demand in a process plant. Total Site Heat Integration for multiple processes (industrial cluster) has been introduced, as an extension to Pinch Analysis, to debottleneck the limitation of energy recovery in a single process plant. The availability of low temperature waste heat could be found, after considering the maximum heat recovery within the industrial cluster through TSHI. Organic Rankine Cycle (ORC) has been widely used for power recovery from low temperature heat sources. ORC integration is frequently being considered for direct heat transfer from a waste heat stream. In this study, integrating ORC via indirect heat transfer for a site utility system is studied, together with the direct integration of ORC to a waste heat stream. The indirect integration provides more opportunity of energy recovery through cumulating the low temperature waste heat from various stream, which has the potential of generating more power than depending on an individual waste heat stream. Economic comparison between direct and indirect ORC integration has been done in this work through a case study. A simple case study shows direct integration has better energy saving opportunity. Indirect ORC integration could be more efficient when the waste heat sources are distributed in different processes and stream