ANNs for Identifying Shock Loads in Continuously Operated Biofilters: Application to Biological Waste Gas Treatment

ANNs for Identifying Shock Loads in Continuously Operated Biofilters: Application to Biological Waste Gas Treatment

Eldon R. Rene (University of La Coruña, Spain), M. Estefanía López (University of La Coruña, Spain), Hung Suck Park (University of Ulsan, South Korea), D. V. S. Murthy (Broward College, USA) and T. Swaminathan (Indian Institute of Technology, India)
DOI: 10.4018/978-1-4666-0294-6.ch004
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Abstract

Among the different waste gas treatment techniques developed to eliminate odorous and toxic pollutants from air, biological techniques have emerged as an effective, reliable, eco-friendly, simple, and economical option. Biological waste gas treatment systems such as biofilters are commonly used in industrial complexes to handle emissions at high gas flow rates and low pollutant concentrations (<5 g/m3). However, from a practical view-point, variation in concentrations and gas flow rates are common to any industrial emission, and it is a pre-requisite to simulate these conditions (shock loads) at the laboratory scale. This chapter provides sufficient theoretical background information on the different waste gas treatment systems, literature review on shock loads in biofilters, and the different steady and transient state models developed in the field of biofiltration. A fundamental overview of artificial neural networks and the different steps of the modeling process are also presented.
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Introduction

Volatile Organic Compounds (VOCs)

The issue of air pollution has been a topic of great importance, and one of the most urgent environmental problems to be solved worldwide. Scientific research and technological advancements have contributed tremendously to understand the deleterious effects of air pollutants on human beings and the environment. Over the past few years, several physical and chemical technologies have been developed to remove a wide variety of air pollutants that include; NOx, SOx, carbon monoxide, hydrogen sulfide, ammonia, mercaptans, methane, volatile hydrocarbons and numerous volatile organic compounds (VOCs), amongst others, from both natural and industrial sources. According to the United States Environmental Protection Agency (US-EPA), VOC can be defined as ``any compound of carbon, excluding carbon monoxide, carbon dioxide, carbonic acid, metallic carbides or carbonates, and ammonium carbonate, which participates in atmospheric photochemical reactions``. Typical products that has the ability to release these VOCs include; paints and lacquers, paint strippers, cleaning supplies, pesticides, building materials and furnishings, office equipment such as copiers and printers, correction fluids and carbonless copy paper, graphics and craft materials including glues and adhesives, permanent markers, and photographic solutions. Among the list of different chemicals mentioned in the Clean Air Act of 1990 (US-EPA), benzene, toluene and xylene (collectively called as ``BTX``) are more commonly found in emissions from petrochemical and pharmaceutical industries. The health effects of these VOCs have been well documented in the literature. For example, chronic poisoning due to toluene inhalation occurs at about 200-400 mg/m3, while impairment of reaction time was observed in volunteers exposed to 870 mg/m3 of xylene vapours for 3 hours (WHO, 1986). The physical and chemical properties of BTX compounds are shown in Table 1.

Table 1.
Properties of individual BTX compounds
PropertyBenzeneaTolueneao,m,p- Xylenea
CAS number
Formula
Relative molecular mass
Flash point, oC
Flammable limits, %
Melting/freezing point, oC
Boiling point, oC
Density, g/ml
Relative vapour density, (air = 1)
Vapour pressure (25 °C), mm Hg
Water solubility, mg/l
Odour threshold, mg/m3
Taste threshold (water), mg/l
Partition coefficient, Log Kow
Sorption coefficient, Log Koc
Henry’s constant, KH in atm m3/mol × 10-3
Retardation factor, R
Threshold Limit Value (TLV-TWA), mg/m3
Conversion factor, 1 ppmv at 25oC in mg/m3
71-43-2
C6H6
78.11
-11.1
1.3-7.1
5.5
80.1
0.878
2.7
95.19
1791-1800
4.8-15
0.5-4.5
1.56-2.15
1.8-1.9
5.43
1.76-22.0
32
4.35
108-88-3
C7H8
92.16
4.4
1.17-7.1
-95
110.6
0.867
3.2
28.4
535
9.4
0.14
2.69
1.12-2.85
5.94
1.82-45.9
188
3.75
1330-20-7
C8H10
106.16
25
1.1-9
13.3
138.3
0.857
1.03
6.6-8.7
146-175
4.35
0.3-1
3.12-3.2
1.68-3.15
5.1-7.68
2.97-88.6
434
3.2

aAdapted from: Mackay et al., (1992); Spicer et al., (2002); Seagren and Becker (2002); Namkoong et al., (2003).

Key Terms in this Chapter

Volatile Organic Compound (VOC): Any chemical compound that contains carbon chains or rings (and also containing hydrogen) with a vapour pressure greater than 2 mm of Hg (0.27 kPa) at 25oC, excluding methane, can be categorized as a volatile organic compound (VOC). However, there are some exceptions. Carbon monoxide (CO2), carbon dioxide (CO), carbonic acid (H2CO3), metallic carbides and carbonate salts are excluded from this list.

Performance Parameters: The performance of any biological waste gas treatment system can be represented in terms of pollutant removal efficiency (%), the amount of pollutant removed per cubic meter of reactor volume per hour (g/m3.h) and by the critical load to the system.

Biofiltration: Biofiltration is an efficient and economically viable air pollution control technique, wherein gas phase pollutants (industrial emissions) are biodegraded to innocuous end-products such as carbon dioxide, water, in a filter matrix that contains a thriving microbial consortium.

Backpropagation algorithm: The backpropagation algorithm uses the method of gradient descent in order to minimize the error function. The combination of weights which minimizes the error function is considered to be a solution of the learning problem.

Mathematical Model: They are a set of mathematical equations that explain the behaviour of the system under various operating conditions, and determine the dominant factors that govern the rules of the process. Mathematical modeling is also associated with data collection, data interpretation, parameter estimation, optimization, and provide tools for identifying possible approaches to control and for assessing the potential impact of different intervention measures.

Artificial Neural Networks (ANNs): Artificial neural networks are collections of mathematical models that emulate some of the observed properties of biological nervous systems and draw on the analogies of adaptive biological learning. The key element of the ANN paradigm is the novel structure of the information processing system. In the computational world, ANNs are also known as connectionist architectures, parallel distributed processing, and neuromorphic systems.

Shock Loads: A sudden, yet unexpected variation in pollutant loading rate that could cause an alteration in the performance of the biological treatment system. These variations could be in the form of increasing or decreasing pollutant (VOC) concentrations, and a sudden change in gas flow rate.

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