**Plain Bearings**

**Chapter 1**

© 2012 Chauhan and Sehgal, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Chauhan and Sehgal, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

The basic configuration of the circular journal bearing consists of a journal which rotates relative to the bearing which is also known as bush (Fig. 1). Efficient operation of such

**Thermal Studies of Non-Circular Journal Bearing**

Hydrodynamic journal bearings are defined as the mechanical components that support the external loads smoothly due to geometry and relative motion of mating surfaces in the presence of a thick film of lubricant. Hydrodynamic journal bearings are extensively used in high speed rotating machines because of their low friction, high load capacity, and good damping characteristics. Such bearings have many different designs to compensate for differing load requirements, machine speeds, cost, and dynamic properties. One unique disadvantage which consumes much time towards the research and experimentation is an instability which manifests itself as oil whip which is a vibration phenomenon. Oil whip is disastrous because the rotor cannot form a stable wedge and consequently this leads to metal to metal contact between the rotor and the bearing surface. Once surface contact exists the rotors begins to precess, in a reverse direction from the actual rotor rotation direction, using the entire bearing clearance. This condition leads to high friction levels which will overheat the bearing metal thus causing rapid destruction of the bearing, rotor journal and machine seals. Fuller [1956] has suggested that the fluid film bearings are probably the most important mechanical components in the recent technological development and are comparable in their significance to the effect of electricity. The development of fluid film lubrication mechanisms has been observed by Petrov [1883] in Russia and Tower [1883] in England. In 1886, Reynolds presented his classical analysis of bearing hydrodynamics, which forms the basis of present days bearing study. The overview of both the circular and non-circular hydrodynamic journal bearings and their design methodologies are discussed

**Profiles: Offset-Halves and Elliptical**

Amit Chauhan and Rakesh Sehgal

http://dx.doi.org/10.5772/51261

**1. Introduction**

as follows:

**1.1. Circular journal bearing**

Additional information is available at the end of the chapter


**6. Conclusions**

Camphor and silver derivatives have been used since ancient times as medicines. Although their combination in complexes and the search for their antimicrobial activities only started recently, there is no application of silver camphor imine complexes in medicine, pharmacy, or industry. The results obtained until now show that some silver camphor imine complexes combine antimicrobial activity against bacteria and fungi with cytotoxic activity [42–44]. This feature is highly relevant since opportunistic bacteria and fungi usually develop during cancer treatments due to the immunosuppressive effects of anticancer drugs.

The insights already made into the antimicrobial properties of some silver camphor imine complexes show they have moderate to high antibacterial activity, depending on the characteristics of the ligands. Such information fosters further enhancement of the ligands to optimize the antibacterial properties of the camphor imine complexes. Meanwhile, synthetic strategies to synthesize, characterize, and evaluate the antimicrobial properties of silver camphor imine complexes were developed and described in the present minireview. Detailed knowledge of the bacterial targets of these compounds is still missing, and therefore, future work will focus on the unveiling of the molecular targets and mechanisms underlying their antimicrobial activity. To address these issues, studies will focus on the use of transcriptomic approaches to gain clues regarding the bacterial gene expression responses to exposure to inhibitory concentrations of silver camphor imine complexes. It will be also necessary to use more classical biochemical approaches to identify the bacterial targets of these complexes. The comprehensive knowledge of the molecular details of the antimicrobial activity of silver camphor imine complexes is expected to enable the exploitation of structure and activity relationships to tailor complexes with enhanced antimicrobial activity.

**Author Contributions:** Conceptualization, J.H.L., M.F.N.N.C.; Writing-Original Draft Preparation, J.H.L., M.F.N.N.C., S.A.S., S.A.L.; Writing-Review & Editing, J.H.L., M.F.N.N.C., S.A.S.; Funding Acquisition, J.H.L., M.F.N.N.C.

**Funding:** This research was funded by from FCT-Fundação para a Ciência e a Tecnologia (Portugal) through projects UID/QUI/00100/2013, UID/BIO/04565/2013, Programa Operacional Regional de Lisboa 2020 (Project N. 007317) and the NMR Network (IST-UTL Node) for facilities.

**Acknowledgments:** S.A.S. acknowledges a postdoctoral grant from FCT (SFRH/BPD/102006/2014).

**Conflicts of Interest:** The authors declare no conflict of interest.


**Acknowledgements**

*Use of Gamma Radiation Techniques in Peaceful Applications*

**5.3 Contour mapping behavior**

**Figure 20.**

estimation algorithm was calculated using the 1/*R*2

The swarm was restricted to move along the *x*-axis and the reference position generation was bound to ±0.5 m, in order to minimize risk of the swarm crashing into the walls of the flight volume. The tracking of the light source and three UAS platforms was carried out using the OptiTrack. Note that an embedded window to display source-seeking performance in real time using the motion capture system in the flight volume is shown in **Figure 19a**. **Figure 19b** shows how the UAS swarm's center moves as the source is moved. As expected, the oscillatory motion of the swarm's center occurs due to gradient estimation errors using a finite number of light sensors.

*(a) Contour mapping experiment with the swarm of three Crazyflie UAS (circled in green) and a virtual source (circled in red) along with a real-time data display window and (b) trajectory of the swarm's center.*

To demonstrate the effectiveness of the contour mapping algorithm in the indoor flight volume, three Crazyflie platforms were used. A virtual source was used due to a limited payload and communication capability of the Crazyflie UAS. As shown in **Figure 20a**, a virtual source was located on the ground and the OptitTrack tracked the source and each UAS in the swarm. The 'source strength' needed for the gradient

the virtual source position data from each UAS. **Figure 20b** shows the experimental results on a plot of motion of the swarm's center following the reference contour defined with respect to the virtual source located on the floor. It maps the reference

Ambient temperature CZT and CLYC sensors were integrated onto the UAS platform using the plug-and-play approach. The CZT sensor was designed for high resolution gamma spectroscopy. The CLYC sensor enables gamma and neutron measurements with an excellent neutron-gamma pulse shape discrimination with the figure of merit 2.3. The automated spectral analysis code locating peaks and

USB hardware connections were used to link the sensors and the main controller with the UAS power source. ROS was used for the data communication and data fusion. To streamline the process of bridging disparate components into a cohesive network, the collection of libraries describing the publisher/subscriber

contour within ±0.1 m, which is less than 8% of the size of the contour.

calculating their intensities was developed for both sensors.

model where *R* was obtained from

**102**

**6. Conclusion**

This work was supported by the Department of Energy Minority Serving Institution Partnership (MSIPP) managed by the Savannah River National Laboratory under SRNS contract MSIT00016.


*3.1. Temperature Profile, Maximum Temperature, and Surface Temperature*

In this section, the temperature profile, maximum temperature and surface temperature are predicted and compared to the experimental results. A moving heat source analysis is used in order to predict the temperature distribution associated with the dynamic heat deposition. The explicit and closed-form temperature solutions are calculated in Section 2.1. The general differential equation of heat conduction in the 2D plane is used. In order to consider the moving heat source, it is assumed that the coordinate system moves with the heat source by using a transformation as shown in Equation (3). Finally, using the separation of variables, the closed-form solution of temperature is obtained in Equation (5). The material properties are assumed to be temperature dependent. The melting/solidification phase change is also considered. The analytical and numerical analysis are conducted in this work.

In order to validate the proposed model, the experimental temperature data are used from the work of Pauzet [35]. The Ti-6Al-4V samples are manufactured using the DMD machine. The dimensions of the samples are 2 mm in width, 70 mm in depth and 80 mm in length. The temperature on the build part surface is measured using the thermocouple of type K. In order to control the experimental setup, the authors used a thermal-camera and a high-speed camera to provide comparison bases for the temperature and the melt-pool size. The DMD machine has used the laser with the wavelength of 1030 nm. The scanning speed of 0.2 m/min and 0.4 m/min and the laser power of 400 W and 600 W are studied. The initial temperature of each layer depends on the final temperature of the previous layer, as the process is multi-layered.

Figure 5 shows the temperature profile of the build part. The temperature is predicted using both the analytical model and the numerical model. The laser moves along the *x*-axis from left to right. The small red spot on top shows the laser location. The layer thickness is chosen to be 80 μm. The distance of the laser from the powder is 0.4 mm. For the same power, as the velocity is increased the maximum temperature is decreased since the powder has less time to absorb the energy. Different combinations of the process parameters are presented in Figure 5, specifically scanning speed and laser power.

**Figure 5.** Predicted temperature profile using (**a**–**d**) physics-based modeling and (**e**–**h**) numerical modeling.

The evolution of the surface temperature is plotted as a function of time for each case as shown in Figure 6. A study point will be chosen from the 2D geometry. When the laser is far away from the study point, the powder is at room temperature. As the laser approaches the study point, the temperature increases continuously. The maximum temperature on the curve corresponds to the moment that the laser is above the study point. After the laser passes the point, the temperature is decreased which shows that the material is cooling down. As shown in these plots, the cooling rate in the AM process is substantially high.

**Figure 6.** Evolution of surface temperature as a function of time for (**a**) P = 400 W, V = 0.4 m/min, (**b**) P = 600 W, V = 0.4 m/min, (**c**) P = 400 W, V = 0.2 m/min, (**d**) P = 600 W, V = 0.2 m/min.

In order to understand the influence of the process parameters on the maximum temperature, and surface temperature, a sensitivity study is designed to investigate both the scan speed and laser power. The short computational time associated with the analytical modeling approach allows for a better understanding of the influence of the process parameters as discussed previously. Figure 7 depicts the influence of the scan speed and laser power on temperature, as predicted by the analytical model and compared to the experimental results.

The results of the simulations from the analytical model illustrates that the maximum temperature decreases linearly as the scan speed increases since the material has less time to absorb the energy. On the other hand, for the fixed scanning speed, as the power increases the maximum temperature increases. The four experimental data are also pointed in Figure 7. The predicted temperature from the analytical model is slightly higher than the experimental values. This error is mainly because the temperature is measured using thermocouples which are a little below the surface.

**Figure 7.** Effect of scan speed and laser power on peak temperature.

Figure 8 represents the influence of the laser on the surface temperature. As the power increases from 200 W to 600 W, the surface temperature increases for a fix scanning speed. On the other hand, the surface temperature will decrease as the scanning velocity increases from 0.1 m/min to 0.6 m/min for a fix laser power as shown in Figure 9.

**Figure 8.** Comparison of evolution of surface temperature for (**a**) V = 0.3 m/min, and (**b**) V = 0.6 m/min.

**Figure 9.** Comparison of evolution of surface temperature for (**a**) P = 400 W, and (**b**) P = 600 W.

As explained, the proposed model considers the multi-layer aspects of metal additive manufacturing. The effect of considering the layer addition on peak temperature is compared to the obtained peak temperature without considering the layer addition, and also compared to the experimental results.

To further validate the proposed model, the peak temperature is plotted as a function of scanning speed for different laser powers. Two different values of laser power (400 W and 600 W) and scanning speed (0.2 m/min and 0.4 m/min) are chosen. The temperature considering the layer addition, the temperature not considering the layer addition, and also experimental values are compared. The values are listed in Table 3. The observations show that considering layer addition improves the prediction of temperature, as shown in Figure 10. For example, the predicted temperature for scanning velocity of 0.2 m/min and laser power of 400 W without considering the layer addition is 2042 ◦C, but when considering the layering aspect of AM, the predicted temperature reduces to 1802.8 ◦C which shows that it affects the heat transfer mechanisms.

**Table 3.** Comparison of temperature prediction among considering layer addition, not considering the layer addition, and experimental values.

**Figure 10.** Comparison of prediction of temperature with and without considering the layers with experimental values.

A comparison is also conducted among the analytical model considering the layer addition and dwell time, numerical model and experimental values as shown in Figure 11.

**Figure 11.** Comparison of predicted temperature among analytical model, experimental values, and FEA.

Overall, the temperature on the surface in terms of magnitude is well captured by both analytical and numerical approaches. The analytical model better approached the experimental measurements. This comparison shows the capability to accurately predict the temperature profile on the surface using the analytical modeling. The analytical approach also provides the power of a short computational time.

#### *J. Manuf. Mater. Process.* **2018**, *2*, 63

In order to illustrate the importance of considering the temperature dependent material properties, a sensitivity analysis is conducted to compare the predicted surface temperature with and without considering the property's temperature-sensitivity. The obtained results demonstrate a significant difference between them as shown in Figure 12. The thermal conductivity of the Ti-6Al-4V is 6.7 W/m· ◦C which results in a low rate of heat transfer in the build part. However, the thermal conductivity of Ti-6Al-4V varies from 6 to 35 W/m· ◦C with respect to temperature. The increase in heat transfer rate induced by the increase in thermal conductivity, causes the predicted surface temperature decrease. In the cases that the temperature sensitivity of the material properties is considered, as the velocity increases from 0.2 m/min to 0.4 m/min, the variation of predicted surface temperature is less than 100 ◦C. However, when the temperature sensitivity of material properties is not considered, the variation of temperature is more than 1000 ◦C. As it is shown in Figure 12 the predicted temperature can be quite unrealistic without considering the material properties sensitivity to temperature.

**Figure 12.** Comparison of predicted temperature considering the temperature sensitivity of material properties (WMTS), and without temperature sensitivity of material properties (WoMTS).


**2. Research review**

### **2.1 Toxic effect of some heavy metals**

Out of the 92 naturally occurring elements, 30 are recognized to be potentially harmful to humans. These are created by anthropogenic or natural processes, but the industrial discharge that is of concern in this case is of particular note [7]. Additionally, it is well recognized that the heavy metal pollution chain moves in a circular pattern from industry to atmosphere to soil to water to food to people. Heavy metals can be harmful even at relatively low levels, despite the fact that toxicity is a function of concentrations. The importance of human exposure, consumption, and absorption was emphasized, particularly in industrialized nations.

### **2.2 Chromium (Cr)**

One of the heavy metals whose concentration continually rises as a result of industrial expansion, particularly the growth of the chemical and tanning industries, is chromium. Electroplating, leather tanning, wood preservation, pulp processing, steel manufacture, and many other operations release chromium into the environment, and the concentrations of chromium and nickel in the environment vary greatly. The greater use of these two metals in emerging nations and their non-degradability raise serious concerns [1]. The human body is carcinogenic and highly soluble in hexavalent chromium. It is also well known that the metallurgies, refractory, chemical, and tannery sectors employ this same hexavalent chromium extensively.

### **2.3 Iron (Fe)**

A heavy metal in the first row of transition metals, iron is one of them. Although Fe2+ is also detected, Fe3+ is the main form that is seen. Iron serves as an oxidizing and reducing agent in the porphyrin enzyme of respiration (Vines and Rees).

*Environmental Impact of Heavy Metals DOI: http://dx.doi.org/10.5772/intechopen.108973*

Seawater contains roughly 3.5 ppm of iron, the fourth most prevalent element in the earth's crust [1]. It reacts fairly quickly. The transition metal (heavy metal) iron is by far the most common and significant one that has a purpose in living systems. Proteins that contain iron take part in the transport of oxygen and the transfer of electrons, respectively. There are other molecules, [4] whose job it is to transmit and store iron. Ferritin and albumin serve as the storage proteins in humans and many other higher animals.

### **2.4 Nickel (Ni)**

Nickel is the most useful element in soil and plant research. Nickel appears to be required for the growth of marine micro algae. The effect of food containing very low concentrations of nickel (e.g., 40.00 ug/g) includes impaired liver metabolism, decreased iron absorption, and decreased activity of many enzymes. The average concentration of nickel in the world's soil is 40.00 mg/g [7]. In the absence of the emission effect, dietary nickel intake was estimated to be 16,511 ug/day on average [8].

The toxicity of nickel is determined by the route of exposure and the solubility of the nickel compound. Epidemiology studies have shown that occupational inhalation exposure to nickel (Ni) dust can result in an increase in pulmonary and nasal cancer [9].

### **2.5 Zinc (Zn)**

Zinc is a heavy metal in the periodic table's first row of transition metals. Zinc is found everywhere and has been shown to be a growth factor in plants and some rodents. Its absence causes mottled leaf disease in fruit trees. Zinc is found in mammalian enzymes such as carbonic anhydrase. It is required for protein metabolism and appears to be involved in the production of chlorophyll in some way. Zinc is essential for plant growth due to its role in auxin formation and as a component of certain enzymes [9]. Zinc is required for the synthesis of the molecule tryptophan, from which auxin is produced.

Several crop disorders were reported in the early 1900s that have now been identified as zinc deficiencies [9]. A thorough investigation of zinc deficiency in all plants reveals, among other symptoms, some form of leaf chlorosis, mostly on veins and ranging in color from white to light green.

Zinc deficiency is common in soils with abnormally high levels of soluble or total phosphates. An early study on Tung's tree zinc deficiency in fluoride concluded that high phosphate in soils was an important factor reducing available zinc [10]. A relatively low concentration of the element in the body can cause heavy metal toxicity, most commonly intestinal distress.

### **3. Environmental pollution**

Through their bonds with sulfhydryl groups and the production of ROS, heavy metals cause toxicity in biological systems. In addition to oxidative stress and glutathione depletion, this results in the inactivation of important macromolecules. There are a number of events that take place once hazardous metals enter the body and are exposed to them, including interactions with or inhibitions of certain metabolic pathways [10, 11]. Multiple negative consequences on both people and animals are consequently seen. Congenital disorders, immune system problems, hormone changes,

particular organ dysfunctions, metabolic abnormalities, cancer, and congenital disorders are a few of these [11]. The presence of metals in the environment, food supply, and drinking water is therefore regulated by a number of international organizations. Studies on risk assessment examine if heavy metals are present in food and water. Nearly 21% of them had amounts that could be detected, it was discovered.

### **4. Solubility of metals and metal compounds**

Chemical speciation affects the environment's metal and metal compound solubility, bioavailability, and persistence; for some metals, speciation may affect the pattern of toxicity (e.g., inorganic arsenic versus organic compounds, inorganic and organic mercury compounds). The papers on exposure concerns and bioavailability and bioaccumulation explore the function of speciation in bioavailability and bioaccumulation within the environment as well as bio-accessibility to human receptors. It is typically believed that the potential toxicity of inorganic species is connected to the cat-existence ions in bodily tissues (in most cases, bound to a tissue ligand). The potential or availability of the metal for interacting at a particular biological target, such as may depend on the intracellular environment and kind of ligand or protein binding [2].

Solubility is one of the most important factors influencing metal and metal compound bioavailability and absorption. A metal compound's solubility is determined by its chemical species, the pH of its medium (H+ ions), and the presence of other chemical species in the medium (see the environmental chemistry paper) [12]. Except for silver, mercury, and lead, nitrates, acetates, and all chlorides of most metals are soluble. Except for barium and lead, most metal sulphates are also soluble. Most hydroxides, carbonates, oxalates, phosphates, and sulphones, on the other hand, are poorly soluble. Particle size is another factor that influences the absorption of poorly soluble compounds: fine particles are usually more soluble. Metallic lead in body tissues (as may occur after gunshot wounds) is most likely absorbed [2].

### **5. Measures of exposure to metals**

In terms of health assessment, the extent of a metal's exposure is best determined by measuring its internal concentration, and even better, the biologically effective dose at the target organ (as opposed to environmental concentration). However, for a variety of reasons, determining the internal or biologically effective dose of the metal at the target tissue is not always feasible. For example, the activity of the heme-synthesizing enzyme aminolaevulinic acid dehydrate (ALAD) in red blood cells is directly related to blood lead concentration and thus may be used as a surrogate for blood lead measurement. The use of biological indicators or markers of exposure, also known as "biomarkers of exposure, " is a method of linking a person's external exposure [13].

### **5.1 Material**

Polyethylene (plastic) bottles, 14 volumetric flasks (100.00 ml), glass funnel, filter paper, 14 beakers (500.00 ml), hot plate pipette, measuring cylinder, hydrochloric acid (HCl), nitric acid (HNO3), Atomic Absorption Spectroscopy (AAS), Conductivity Meter, Turbidity Meter, and pH Meter were used for the analysis.

### **5.2 Methods**

To avoid the risk, the sample was prepared and digested using standard analytical methods with nitric acid (HNO3) and hydrochloric acid (HCl) at relatively low temperatures, as reported by [1].

The powerful solvent used was aqua regia, a mixture of hydrochloric acid and nitric acid (10:1 V/V). 100.00 ml of each sample was measured and transferred to a 500.00 ml beaker, followed by 10.00 ml of hydrochloric acid (HCl) and 1.00 ml of nitric acid (HNO3). The mixture was then heated on a hot plate for about 3 hours at a relatively low temperature (200C-500C) (NB: Do not allow it to boil) until it was reduced to about 20.00 ml.

The mixtures were then cooled and filtered in a 100.00 ml volumetric flask using a glass funnel and filter paper, and then diluted to volume (i.e., distilled water was added to the mark of the volumetric flask).At the quality control laboratory, the mixtures were tested using Atomic Absorption Spectroscopy (AAS). Based on the above procedure, we reanalyzed the five different samples for the following heavy metals: chromium (Cr), iron (Fe), nickel (Ni), and zinc (Zn).


*4.5. Proteomic Data Acquisition*

The proteomic data acquisition approach was modified from Haddad Momeni et al. [48]. Briefly, peptides were loaded onto a 2 cm C18 trap column (ThermoFisher 164705), connected in-line to a 15 cm C18 reverse-phase analytical column (Thermo EasySpray ES803) using 100% Buffer A (0.1% Formic acid in water) at 750 bar, using the Thermo EasyLC 1200 HPLC system (ThermoFisher, Roskilde, Denmark), and the column oven operating at 30 ◦C. Peptides were eluted over a 140 min gradient ranging from 10 to 60% of 80% acetonitrile, 0.1% formic acid at 250 nL/min, and a Q-Exactive instrument (ThermoFisher Scientific, Roskilde, Denmark) was run in a DD-MS2 top 10 method. Full MS spectra were collected at a resolution of 70,000, with an automatic gain control (AGC) target of 3 × 106 or maximum injection time of 20 ms and a scan range of 300 to 1750 *m*/*z*. The MS2 spectra were obtained at a resolution of 17,500, with an AGC target value of 1 × 106 or maximum injection time

of 60 ms, a normalized collision energy of 25, and an intensity threshold of 1.7 × 104. Dynamic exclusion was set to 60 s, and ions with a charge state <2 or unknown were excluded. MS performance was verified for consistency by running complex cell lysate quality control standards, and chromatography was monitored to check for reproducibility. The mass spectrometry data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org (accessed on 21 December 2020)) via the PRIDE partner repository with the dataset identifier PXD 023249. The reviewer account details: Username<EMAIL_ADDRESS>Password: xteOmcDd. The raw files were analyzed using Proteome Discoverer 2.4. Label-free quantitation (LFQ) was enabled in the processing and consensus steps, and spectra were matched against the *P. rubra* S4059 database obtained from Uniprot (UniProt ID: UP000305729). Dynamic modifications were set as Oxidation (M), Deamidation (N, Q), and Acetyl on the protein N-termini. Cysteine carbamidomethyl was set as a static modification.

The methods of proteomic analyses were modified from Beyene et al. [49]. Briefly, all the statistical analyses were performed using Perseus software (version <IP_ADDRESS>, Max-Planck Institute of Biochemistry, Martinsried, Germany), https://maxquant.net/perseus/ (accessed on 20 September 2020)), and all results were filtered to a 1% false discovery rate (FDR). The normalized abundance values for each protein were log2 transformed, and at least two valid values were required in the bio-triplicates for quantitation. When the original signals were zero, they were imputed with random numbers from a normal distribution, in which the mean and standard deviation were chosen from low abundance values below the noise level (Width = 0.3; shift = 1.8) [49,50]. To identify proteins with significantly different abundances when S4059 were grown on chitin compared to on mannose, the FDR were estimated using Benjamini–Hochberg method, and a two-tailed unpaired *t*-test was used with a combination of *p*-value ≤ 0.05 and Log2 fold-change ≥1.5 [51]. The resulting significant proteins were exported from Perseus and visualized in GraphPad Prism 8 (Graphpad Software, San Diego, CA, USA, https://www.graphpad.com/scientific-software/prism/ (accessed 5 October 2020)) using volcano plots.


**DAM ENGINEERING**

#### **Dam Engineering**

http://dx.doi.org/10.5772/intechopen.74153 Edited by Hasan Tosun

#### **Contributors**

Akira Kobayashi, Akira Murakami, Arash Barjasteh, Aleksandra Ziemińska-Stolarska, Abdikerim Irisbaevich Kurbanaliev, Amanbek Zhainakov, Maralbek Oskonbaev, Zhongzhi Fu, Shengshui Chen, Enyue Ji

#### **© The Editor(s) and the Author(s) 2019**

The rights of the editor(s) and the author(s) have been asserted in accordance with the Copyright, Designs and Patents Act 1988. All rights to the book as a whole are reserved by INTECHOPEN LIMITED. The book as a whole (compilation) cannot be reproduced, distributed or used for commercial or non-commercial purposes without INTECHOPEN LIMITED's written permission. Enquiries concerning the use of the book should be directed to INTECHOPEN LIMITED rights and permissions department (permissions@intechopen.com). Violations are liable to prosecution under the governing Copyright Law.

Individual chapters of this publication are distributed under the terms of the Creative Commons Attribution 3.0 Unported License which permits commercial use, distribution and reproduction of the individual chapters, provided the original author(s) and source publication are appropriately acknowledged. If so indicated, certain images may not be included under the Creative Commons license. In such cases users will need to obtain permission from the license holder to reproduce the material. More details and guidelines concerning content reuse and adaptation can be foundat http://www.intechopen.com/copyright-policy.html.

#### **Notice**

Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

First published in London, United Kingdom, 2019 by IntechOpen eBook (PDF) Published by IntechOpen, 2019 IntechOpen is the global imprint of INTECHOPEN LIMITED, registered in England and Wales, registration number: 11086078, The Shard, 25th floor, 32 London Bridge Street London, SE19SG – United Kingdom Printed in Croatia

British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library

Additional hard and PDF copies can be obtained from<EMAIL_ADDRESS>
Dam Engineering Edited by Hasan Tosun p. cm. Print ISBN 978-1-78985-479-4 Online ISBN 978-1-78985-480-0 eBook (PDF) ISBN 978-1-83962-037-9


**Author details**

Katsuyuki Takahashi1,2\*, Koichi Takaki1,2 and Naoya Satta3

1 Faculty of Science and Engineering, Iwate University, Morioka, Japan

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*A Novel Wastewater Treatment Method Using Electrical Pulsed Discharge Plasma over a Water… DOI: http://dx.doi.org/10.5772/intechopen.101494*