Bharat B. Gulyani

Estimating Number of Shells in Shell and Tube Heat Exchangers: A New Approach Based on Temperature Cross

Multipass heat exchangers are often designed by using the rule of thumb F T ≥0.75, which is rather arbitrary. F T falls sharply with the increase in temperature cross. Hence, only a limited temperature cross can be allowed. The ability to accommodate temperature cross increases rapidly as the number of shell passes is increased. Though many investigators have emphasized the importance of temperature cross in exchanger design, it has as yet not been explicitly accounted for in the design. This paper introduces a new approach for estimating the number shells in a shell and tube exchanger which directly accounts for temperature cross, rather than routing this effect through F T or Xp (Ahmad et al.s parameter, which is again a correction factor not directly related to temperature cross). The approach is compatible with the established design procedures and bypasses the F T . It generates better designs by defining maximum permissible temperature cross, than the traditional designs based on specifying minimum permissible F T . Expressions have also been provided which correlate the present formulation with that of Ahmad et al.

An ensemble method for predicting biochemical oxygen demand in river water using data mining techniques

ABSTRACT Biochemical oxygen demand (BOD) is used to determine the amount of dissolved oxygen used by microbial oxidation of organic content. BOD is a parameter describing the quality of water, especially its extent of pollution. Water from wastewater treatment plants have high BOD values and as such require to be treated. For this purpose, BOD is used as an indicator in determining the quality of water being discharged. The standard method for measuring BOD is a 5-day process. Dilution of sample, constant pH and nutrient content besides temperature of 20°C and dark area are required for correct results. High levels of nitrogen compounds yield false BOD results. Winkler titration, which is also used to measure DO (as part of BOD measurement), is a chemical-intensive process. Hence an automatic prediction model for BOD is required for accurate, cost-effective and time-saving measurement. Based on data available for BOD measurements, the present study focuses on devising a prediction model for BOD using ensemble techniques in data mining. A correlation coefficient of 0.9541 and a root mean-squared error of 0.4679 were obtained for the proposed BOD prediction model on river water quality data. Comparative analysis of the proposed model with existing models built for the same data set was also performed.

Usage of Date Stones as Adsorbents: A Review

In this article, the use of date stones (thrown as a waste material) as adsorbent for the removal of a variety of adsorbates in aqueous and gaseous streams have been reviewed. Adsorption plays a role in the wastewater treatment as a polishing process, especially on activated carbon at tertiary treatments. In this review, the preparation and characterization technique along with the applications of the date stones as adsorbent has been presented in detail. A comprehensive study and the comparison of the available data in literature reveal that the date stones can be used as a potential adsorbent for a wide variety of toxic contaminants such as dyes, heavy metals, insecticides, etc.

A new approach for shell targeting of a heat exchanger network

Abstract A new approach is developed for targeting number of shells of a heat exchanger network, which directly accounts for the temperature cross. It is based on R and G values, where G is a dimensionless group defined to account the temperature cross. For this purpose design charts of FT(R,S) and FT(R,G) are developed and superimposed to create a single chart for each flow configuration. Here, R and S are two dimensionless groups called heat capacity flow rates and thermal effectiveness, respectively. In the present paper a chart for 1–2 shell and tube heat exchanger is shown. To show the reliability of this approach it is used to target number of shells for different heat exchanger network problems taken from open literature and the results are compared to that predicted from published methods. It is found that the present approach computes the number of shells with considerably less effort.

Induction of Model Trees for Predicting BOD in River Water: A Data Mining Perspective

Water is a primary natural resource and its quality is negatively affected by various anthropogenic activities. Deterioration of water bodies has triggered serious management efforts by many countries. BOD is an important water quality parameter as it measures the amount of biodegradable organic matter in water. Testing for BOD is a time-consuming task as it takes 5 days from data collection to analyzing with lengthy incubation of samples. Also, interpolations of BOD results and their implications are mired in uncertainties. So, there is a need for suitable secondary (indirect) method for predicting BOD. A model tree for predicting BOD in river water from a data mining perspective is proposed in this paper. The proposed model is also compared with two other tree based predictive methods namely decision stump and regression trees. The predictive accuracy of the models is evaluated using two metrics namely correlation coefficient and RMSE. Results show that the model tree has a correlation coefficient of 0.9397 which is higher than the other two methods. It also has the least RMSE of 0.5339 among these models.

An Approach for Predicting River Water Quality Using Data Mining Technique

Water contains many chemical, physical, and biological impurities. Some impurities are benign while others are toxic. The quality of water is defined in terms of its physical, chemical, and biological parameters and ascertaining its quality is crucial before use for various intended purposes such as potable water, agricultural, industrial, etc. Various water analysis methods are employed to determine water quality parameters such as DO, COD, BOD, pH, TDS, salinity, chlorophyll-a, coli form, and organic contaminants such as pesticides. The list of potential water contaminants is exhaustive and impractical to test for in its entirety. Such water testing is sometimes costly and time consuming. This paper attempts to present application of data mining technique to build a model to predict a widely used gross water quality parameter called Biochemical oxygen demand (BOD). BOD is a measure of the amount of dissolved oxygen used by microbial oxidation of organic matter in wastewater. The standard method for measuring BOD is a 5-day process. Dilution of sample, constant pH and nutrient content besides the temperature of 20 °C and dark area are required for correct results. High levels of nitrogen compounds yield false BOD results. Winkler titration which is also used to measure BOD is a chemical intensive process. Hence an automatic prediction model for BOD has been sought for accurate, cost-effective and time saving measurement. Based on data available for BOD measurements, this paper describes the development of a prediction model for BOD using a technique of data mining, namely, support vector machines (SVM). A correlation coefficient of 0.9471 and RMSE of 0.5019 was obtained for the BOD prediction model on river water quality data. The performance of the proposed model was also compared with two other models namely artificial neural network (ANN) and regression by discretization. Simulation results show that the proposed model performs better than the other two in terms of correlation coefficient and RMSE.