Foundation
Water Contaminants
Chapter 1 Water Treatment 3 TABLE CD1.2 Treatment Technology Matrix (Excerpt from Table CD1.2b) 7
This article presents a comparison between finite difference methods and mathematical solutions for continuous salt input, as detailed in Table CD4.2(a) It further explores the finite difference mass balance equation for continuous salt input scenarios, outlined in Table CD4.2(b) Additionally, Table CD4.3 addresses the finite difference equation in the context of pulse input of salt, providing insights into the variations in outcomes based on different input types.
TABLE CD5.2 Bar Screen Design Based on Hydraulic Criteria 81 TABLE CD5.7 Microscreen Coefficient and Subsequent Use of Coefficient for Design
(Data from Envirex, 1985)—Excerpt Showing 24 Out of 40 Columns and 5 Out of 39 Rows 88
TABLE CDEx6.1 Determination of Maximum Particle Size for Stokes’Law to be Applicable 97 TABLE CD6.5 Materials Balance Calculations for Area of a Final Settling Basin 112
The article includes critical tables and figures that provide essential data for various engineering calculations related to water treatment systems Table CD7.1 presents the solution for Shield’s Equation concerning scour in grit chambers, while Table CD7.2 outlines the spreadsheet used for proportional weir sizing and flow calculations Additional tables, such as CD7.3, detail free flow ranges and coefficients for Parshall flumes, and CD7.4 offers hydraulic profile calculations Furthermore, Table CD7.5 lists dimensions and capacities for Parshall flumes, and Table CD7.6 focuses on the design of a rectangular grit chamber utilizing a Parshall flume as a control mechanism Table CD7.7 provides a calculated parabolic section for the selected Parshall flume, complemented by Figure CD7.11, which illustrates the calculation of the parabolic section Lastly, Table CD7.14 includes airflow and power calculations specific to aerated grit chambers, ensuring comprehensive guidance for engineers in the design and analysis of these systems.
TABLE CD8.2 Particle Rise Velocities as Function of Number of Bubbles Attached,B 173 TABLE CD8.3 Calculation of Required Saturator Pressure to Float Solids for Stated Conditions by Mass Balance 175
TABLE CD9.6 Distribution of Aluminum Ion Hydrolysis Species with Varying pH 208 TABLE CD9.7 Determining the Distribution of Ferric Iron Hydrolysis Species with Varying pH 209 xli
This article includes essential calculations related to various engineering concepts, such as the determination of n, P, P=V, P=Q, and G based on R and P, as detailed in Table CD10.4 It also presents complete-mix reactor calculations for residual concentrations and mass remaining, found in Table CD10.7 Furthermore, Figure CD10.18 illustrates the residual concentration fraction as a function of time (t) Additionally, Table CD10.12 covers calculations involving jet flow, jet velocity, pipe velocity, power dissipated, and G values, providing a comprehensive overview of these critical parameters.
GuValues, Trajectory from Jet, etc 274 FIGURE CD10.23 Trajectories of two jets 275 TABLE CD10.14 Example of Headloss,P,GCalculations for Static Mixer 279
TABLE CD11.2 Calculation of Floc Specific Gravity by Equation 11.9 300 TABLE CD11.7 Floc Basin Paddle-Wheel Data for Different Motor Controller Settings and Associated
This article presents calculations of torque, power expended, and key parameters for the first compartment of a pilot plant operating at 76 L/min (20 gpm) Additionally, it includes a table detailing the calculation of Camp’s slip coefficient using pilot plant data Furthermore, the article features an animated walkthrough of the flocculation and sedimentation finished designs in Fort Collins.
Water Treatment Plant, 2000 Addition (a) Animation 1 Flocculation Basin–Plate Settlers Walk-Through upstairs—PAK1B AVI (excerpt showsflocculation basins).
(b) Animation II Flocculation Basin–Plate Settlers Walk through downstairs—PAK1A AVI (excerpt shows corridor between basins; paddle-wheel motors are visible on the walls) 317
The article discusses the calculation of transport and filter coefficients using an Excel spreadsheet, as illustrated in TABLE CD12.3 Additionally, TABLE CD12.7 provides insights into bed expansion in relation to backwash superficial velocity and outlines the method for calculating minimum fluidization velocity with necessary corrections.
Chapter 13 Slow Sand Filtration 395 TABLE CD13.2/CDE.2 Conversion betweenKandkIncluding Headloss Calculation fromk 402
The article presents critical data on pressure loss and headloss calculations related to hydraulic variables, as detailed in Table CD14.2 and Table CD14-* Additionally, Figure CD14.10 illustrates headloss versus time plots for five grades of diatomite, derived from the calculations in Table CD14.2 Furthermore, Table CD14.5 provides a comprehensive description of 12 DE plants along with their operating protocols.
PART IV Molecules and Ions
The article includes essential tables for understanding the determination of headloss in GAC columns, detailed design protocols for GAC packed-bed reactors, and Freundlich isotherm coefficients for 141 synthetic organic compounds using Filtrasorb 300 as the adsorbent.
TABLE CD16.A.1 Conversions of Density of Particles 530 TABLE CD16.A.2 Conversions of Ion-Exchange Capacity 531
Chapter 17 Membrane Processes 539 TABLE CD17.6 Calculation of Osmotic Pressure by van’t Hoff Law of Dilute Solutions 555 xlii Contents—Downloadable Files
TABLE CD19.4 Calculation ofCt’s forGiardiaCysts for DifferentC, pH,TConditions (Excerpt) 616 TABLE CD19.6 Concentrations of [Cl 2 ], [HOCL], and [OCl ] as a Function of pH for a Given
[Cl 2 ] Concentration 619 FIGURE CD19.5 Calculations based on 4 mg=L (5.710 5 molar) solution of Cl 2 added to solution
(from mass balance, charge balance, equilibrium relations) as function of pH.
(a) Concentrations of chlorine species (b) Fraction,a, of different chlorine species 620
FIGURE CD21.1 pC versus pH diagram for Ca 2þ 666 TABLE CD21.2 Concentrations of [Ca 2þ ] as a Function of pH 666
TABLE CD23.4 Excerpt from Excel Spreadsheet for Dynamic Activated-Sludge Model 734 FIGURE CD23.6 Flow and BOD over 24 h and calculated effluent BOD, Fort Collins WWTP, 1990.
(a) Influentflow (b) Influent BOD and calculated effluent BOD 734 TABLE CD23.6 Excerpt from Biofilm Trickling-Filter Model Spreadsheet 740 FIGURE CD23.8 Trickling-filter model output 741
The article provides a quick reference for essential constants, units, and conversions, including SI prefixes and coefficients for polynomial and exponential best fit equations It also includes miscellaneous notes to assist in calculations and understanding of variables.
Appendix D Fluid Mechanics—Reviews of Selected Topics 791
The article includes a spreadsheet (TABLE CDD.2(a)) that calculates the pressure surface for an under-drain system, along with an abstract (TABLE CDD.2(b)) detailing head levels and flows in header pipes and laterals for a filter bed Additionally, FIGURE CDD.7 illustrates the pneumatic grade line, highlighting the changes in flow to a submerged diffuser.
(figure is also embedded in Table CDD.3) 801 TABLE CDD.3 Pneumatic Analysis of Air Flow to Aerated Grit Chamber (by Bernoulli Relation) to Obtain
Pressure Inputs to Compressor 805 TABLE CDD.5 Air Flow Calculation for Compressor for an Aerated Grit Chamber (Example) 809 TABLE CDD.6 Combined Pipe Flow and Compression Spreadsheet 810
TABLE CDE.2 Conversion betweenKandkIncluding Headloss Calculation fromk 823 TABLE CDE.4 Hydraulic Gradient Calculated by Forchheimer Equation 830
Appendix F Alum Data and Conversions 833
TABLE CDF.3 Alum Conversions 842 TABLE CDF.3 Alum Conversions (Excerpt Showing only Tables CDF.3(a) and (b)) 842
Appendix G Dimensionless Numbers 847 TABLE CDG.3 Matrix of Physical Phenomena and Associated Dimensionless Numbers 850
Understanding the common themes in unit processes enhances the learning experience of water treatment, transforming it from mere facts and equations into a coherent framework Part I lays the groundwork for this understanding by introducing foundational concepts, which are detailed in the first four chapters.
Chapter 1 explores the disaggregation of water treatment into unit processes, principles, and technologies, highlighting diverse treatment applications It showcases treatment trains that extend beyond conventional municipal potable water and wastewater, encompassing tertiary treatment, modified water treatment, industrial process water, and industrial wastewaters The potential applications in water treatment are virtually limitless.
Chapter 2 examines the diverse range of contaminants present in water, highlighting the necessity of their removal to meet legal requirements and address private needs, such as water for industrial processes.
In Chapter 3, the concept of a "model" is explored through its diverse forms, highlighting its fundamental role in design Models, which encompass everything from mental images to photographs, are integral to our daily lives Designers leverage a variety of resources, including inspections of existing plants, personal judgment, rules of thumb, equations, mathematical models, physical models, and computer animations Collectively, these tools serve as models that facilitate the transition from abstract ideas to practical applications in design and operation.
The concept of a "reactor," as discussed in Chapter 4, is fundamental to various unit processes, signifying that when dissolved or particulate contaminants are passed through a specific "black box," transformative changes take place This reactor principle serves as the foundation for mathematically modeling these changes and is applicable across numerous processes, including settling, mixing, deep bed filtration, adsorption in packed columns, ion-exchange, membranes, gas transfer, disinfection, precipitation, oxidation, activated sludge, and bioreactors Additionally, the concept is relevant to both natural systems, where "passive" changes occur, and engineered systems.
This chapter reviews unit processes in water treatment technology, explores the origins of water treatment, discusses various units, and provides an overview of the book's organization.
Organic Carbon as a Contaminant
Each contaminant in natural waters and municipal or industrial discharges has a distinct narrative regarding its occurrence, impact on various uses, treatment methods for concentration reduction, and evolving regulatory requirements This article focuses on organic carbon to exemplify the complexity and significance of these factors.
The article discusses the 34 fundamentals of water treatment unit processes, focusing on physical, chemical, and biological methods It highlights the extensive research on organic carbon due to the health implications of disinfection by-products (DBPs) in drinking water, which gained attention in the mid-1970s The U.S Environmental Protection Agency (EPA) regulations have played a crucial role in addressing DBPs, leading to increased scrutiny and understanding of these contaminants This review provides a concise overview of the topic.
According to Randtke (1988, p 40), organic contaminants in water may be grouped into three classes:
1.Natural organic matter(NOM): Humic substances, microbial exudates, animal wastes, and products of degraded tissue
2.Synthetic organic chemicals (SOCs): Pesticides, volatile organic chemicals (VOCs), and other chem- icals produced commercially or as waste products of manufacturing
3.Chemical by-products and additives: Substances that enter or are formed during treatment or in the distri- bution system
Natural Organic Matter (NOM) is a traditional contaminant that not only contributes to color but also acts as a precursor to disinfection byproducts (DBPs), which have potential carcinogenic effects, a concern that has persisted since the mid-1970s Furthermore, residual NOM after treatment can promote bacterial growth in distribution systems In response to the health risks associated with DBPs and synthetic organic compounds (SOCs), the United States enacted the Safe Drinking Water Act in 1974 The enforcement of this Act was bolstered by advancements in instrumental analysis methods that enabled the measurement of chemical concentrations in micrograms per liter, particularly through developments in gas chromatography and mass spectrometry, which have also become more cost-effective.
The characterization of organic materials in water varies based on the type of water and its intended use, evolving with emerging issues Wastewaters are traditionally assessed using Biochemical Oxygen Demand (BOD) and Suspended Solids (SS) The BOD test, conducted over five days, measures the biodegradable organic carbon by evaluating the dissolved oxygen levels at the start and end of the incubation period at 20°C, while considering sample dilution with "BOD" water, which is nutrient-rich and oxygen-saturated Although the test does not measure Total Organic Carbon (TOC), BOD can be proportional to TOC for specific wastewaters, highlighting that only a fraction of organic carbon is biodegradable within the five-day timeframe This testing method dates back to around 1900.
In recent years, particularly since 1973, the presence of organic carbon in natural waters used for municipal supplies has become a growing concern Natural organic matter (NOM), primarily consisting of humic and fulvic acids, typically occurs in surface waters at concentrations of 3–6 mg/L, originating from the decomposition of vegetative organic material.
Organic matter primarily consists of humic and fulvic acids, which share similar functional groups with lignins and other plant polymers, though they contain a higher proportion of carboxylic acid groups and exhibit surface-active properties Humus components are derived from plant polymer segments that have been oxidized to form carboxylic acid groups at one or more ends In contrast, unaltered lignin segments are more hydrophobic than these carboxylic groups Molecules that possess both hydrophobic (nonpolar) and hydrophilic (polar) characteristics are known as amphiphiles.
Humification is a process by which biomass consist- ing of dead plant and animal remains is converted to humis; this is one of the basic steps of the carbon cycle.
Organic compounds in plant and animal tissues are thermodynamically unstable in Earth's oxidizing atmosphere, leading to their conversion into carbon dioxide and water through enzymatic processes However, some tissue undergoes partial oxidation, resulting in the accumulation of organic compounds known as humus.
Vascular plants, which possess specialized tissues for water and nutrient transport, are the predominant species in most terrestrial ecosystems Their structural composition includes three main types of polymers: cellulose, hemicellulose, and lignin, along with smaller amounts of aliphatic polyesters, starches, proteins, phenolic macromolecules, and lipids.
The degradation of plant polymers is driven by enzymatic reactions that facilitate depolymerization and oxidation Polysaccharides like cellulose and hemicellulose typically experience hydrolytic depolymerization, while lignin primarily undergoes oxidative degradation Additionally, lipids are subject to both hydrolysis and oxidation processes.
The products from lignin and lipid degradation are, in general, oxidized fragments in which much of the chemical structure of the original polymer is preserved.
Amphiphilic lignin–carbohydrate complexes, found in wood, contribute to the presence of amphiphiles formed through degradation These complexes exhibit number average molecular weights that are significant in their structural properties.
Water Contaminants 35 matter thatfinds its way into ambient waters after rainfall, and of the decay of organic matter within a water body.
Natural Organic Matter (NOM) imparts color to water, quantified in standard color units (SCU) While color itself poses no health risks, it can impact the taste and overall palatability of drinking water Due to its lack of health significance, color was designated as a "secondary standard" under the 1973 Safe Drinking Water Act.
Since the 1920s, color has been recognized as a key factor in assessing drinking water quality, making its reduction a longstanding goal in water treatment processes, typically achieved through coagulation and evaluated using the "jar" test.
In the early 1970s, chlorinated organics were identified as carcinogens (Box 2.A.2) based on reports of higher levels of
THMs in New Orleans drinking water, which precipitated the 1974 Safe Drinking Water Act (SDWA)—the first
A review of how disinfection by-products (DBPs) became a national issue was reviewed by James M.
Symons (2001a,b), who, in the1970s, was Chief of the
Physical and Chemical Contaminants Removal Branch,
Drinking Water Research Division, USEPA, Cincinnati.
This position provided the vantage point of both per- spective and responsibility to provide initiative.
Johannes Rook, a chemist with the Rotterdam Water
In 1971, researchers in Rotterdam identified chloroform in the drinking water while investigating taste and odor sources using a head-space sampling technique Although chloroform was noted among various micropollutants, there was no significant concern raised, as a health officer pointed out that chloroform is a common ingredient in cough syrup and was not recognized as a toxin.
In 1971, EPA chemist Thomas Bellar was tasked with creating an effective method for measuring volatile organic compounds (VOCs) in wastewater He developed the "purge and trap" technique, which complements gas chromatography for accurate VOC analysis.
An analytical breakthrough enabled the detection and measurement of organic contaminants at the mg/L level In his analysis of tap water samples, Bellar identified the presence of chloroform.
Models
In undergraduate education, we often focus on well-defined problems, but real-world scenarios present challenges such as incomplete knowledge, insufficient data, unclear solution methods, and vague objectives This contrasts with the traditional view of engineering as a deterministic field primarily concerned with computation.
This chapter examines some of the approaches for attack- ing problems The concept of modeling is a theme common to all Modeling is an engineering method (Box 3.1).
About 10–15 unit processes comprise thefield of water treat- ment, depending on how they are categorized Perhaps there are 80–100 technologies developed from them Table 3.1 lists
13 unit processes and associated technologies Fundamental principles operative include
Sieving of particles by screens (ranging from bac- teria by membranes to large objects by bar screens)
Creating conditions for application of a ‘‘passive’’ force on particles (e.g., gravity), or an‘‘active’’force
Turbulence and diffusion for the transport of par- ticles to cause contacts between reactants
van der Waals attraction between molecules and a surface (such as activated carbon), or charge attraction
(such as between ions and an ion-exchanger material)
Various chemical reactions such as
Membrane processes involving retention of ions and molecules, i.e., reverse osmosis=nanofiltration
Since around 1900, the unit processes outlined in Table 3.1 have evolved from early technologies like slow sand filtration, first implemented in London in 1829 While proprietary innovations have broadened the range of available technologies, most still represent variations of these foundational unit processes.
A model serves as a representation of a specific aspect of reality, and it is considered "valid" when its predictions align accurately with the corresponding elements of the system it is designed to emulate Typically, the system being modeled, referred to as the "prototype," is a full-scale process Common examples of such systems include activated sludge reactors, biofilm reactors, plate settlers, slow sand filters, and rapid rate filters.
A comprehensive filtration system, including components like a granular activated carbon reactor and an ozone reactor, is essential for maintaining water quality Additionally, natural systems such as streams, lakes, and groundwater can be effectively modeled using mathematical techniques to assess the impact of pollutant discharges on these environments.
Table 3.2 outlines diverse model types, highlighting their unique characteristics along with their advantages and disadvantages It emphasizes that models can encompass various forms, including lore, judgment, descriptions, bench testing, pilot plants, demonstration plants, and mathematical models.
A model serves as a representation, encompassing various forms such as photographs, language, drawings, paintings, maps, plots, equations, and digital data However, for a model to qualify as an engineering model, it must not only represent but also provide utility in bridging the gap between the unknown and the known.
In engineering, models are often conceptualized as pilot plants, coordinated equations in algorithms or spreadsheets, or even as single equations Common engineering practices, such as lore, judgment, extrapolation, bench scale testing, pilot plants, demonstration plants, and mathematical modeling, represent various forms of models that meet established criteria Each of these model forms can be viewed as a "black box," encapsulating complex processes within a simplified framework.
‘‘black box’’accepts a set of‘‘inputs,’’without regard to how it works, and generates outputs.
The proverbial‘‘black box’’has its place as a primary engin- eering method Figure 3.1 depicts the concept of the black
45 box, illustrating the idea of how the values of dependent variables are generated by maintaining y and z constant while varyingx;fandcare measured for each level ofxat
In a systematic exploration of a hypothetical surface, fixed values of y and z are initially established Once y is adjusted to a new value, the process is repeated, and this continues until all values of y are examined Subsequently, z can be modified to a new level, with the same iterative process applied for each z value For instance, with 5 levels of x, 8 levels of y, and 10 levels of z, the total number of experiments conducted would amount to 400 This process demands significant effort to thoroughly investigate all potential combinations.
The traditional jar test exemplifies the process of exploring treatment methods, where numerous experiments are conducted to identify optimal coagulant dosage (x) and polymer dosage (y) under varying seasonal water quality conditions (z).
A "black box" is a device that produces outputs (dependent variables such as f and c) based on selected inputs (independent variables like x, y, and z), illustrating a functional relationship Essentially, any method that generates outputs from inputs can be classified as a "black box," which may encompass judgment, physical models, and mathematical models.
3.2.2.1 Plots The kind of experimental program outlined above might be called ‘‘parametric exploration.’’Figure 3.2 illustrates the output offas a function ofxandy, withzconstant, i.e.,fvx for yẳy1, y2, ,ynand zẳz1, wherez represents a set of conditions that are maintained constant during the testing To be more specific, the system being modeled is a rotating drum microscreen Theflow of water through the screen divided by its submerged area is the velocity of water through the screen, v, which is the ‘‘dependent’’ variable, i.e., f Then v is affected by the independent variables, headloss,h, across the screen, as seen by the curve and the rotational velocity,v, of the drum, in which h and vcorrespond to xand y, respect- ively The set of curves of Figure 3.2 is for all other conditions being maintained constant If, for example, the suspension changes (such as one species of algae instead of another) or the screen size changes, then another set of conditions exists and another set of curves must be generated Thus, a set of one or more plots, such as seen in Figure 3.2, is the end result of a black box experimental program.
A physical model is a scaled-down version of equipment designed to simulate a specific process Its advantage lies in its ability to incorporate unforeseen variables naturally Consequently, the outputs, or dependent variables, represent all independent variables, including those not initially identified.
Smaller-scale models are more cost-effective and simpler to operate compared to full-scale systems, allowing for better control of independent variables This enables a thorough investigation of their effects on dependent variables Examples of physical models include bench scale testing, pilot plants, and demonstration scale plants.
3.2.3.1 Bench Scale Testing Bench scale testing may include jar tests to determine chemical dosages, kinetic coefficients, isotherm constants, and generat- ing relationships between various other kinds of intensive variables The testing is‘‘one dimensional’’in nature, i.e., the intent is to examine the influences of only one or two inde- pendent variables (such as screen size) in selected dependent variables (such as effluent concentration).
3.2.3.2 Pilot Plants One purpose of a pilot plant is to generate functional relation- ships between dependent and independent variables The extent to which this is done, i.e., the scope of the experimental program, depends upon the nature of the problem and the budget available.
Another purpose of a pilot plant study may be to determine coefficients of a mathematical model A mathematical model has greater utility than a set of plots.
Pilot plant experiments will yield, almost without exception, unexpected results that lead to new insights and serendipitous
findings Thus, any plan devised in anticipation of a set of results should haveflexibility to incorporate newfindings.
Modeling has two themes of logic: inductive and deductive, formalized by Sir Francis Bacon (1561–
Bacon extolled observation and practical outcomes, while Descartes believed that pure reasoning was the basis for problem solving (Durant, 1926).
The essence of empiricism is observation Engineer- ing forms include bench scale testing, pilot plants, dem- onstration plants, evaluations of existing plants, etc.
Also included in this category are judgment, lore, and