Scientific Research and Essays Vol. 5(17), pp. 2341-2357, 4 September, 2010 Available online at http://www.academicjournals.org/SRE ISSN 1992-2248 ©2010 Academic Journals
Review
Environmental systems engineering: A state of the art review ChangKyoo Yoo, Abtin Ataei*, YongSu Kim, MinJung Kim, HongBin Liu and JungJin Lim Department of Environmental Science and Engineering, Kyung Hee University, 446-701, Korea. Accepted 7 July, 2010
This paper presented a state-of-the-art review in the field of Environmental Systems Engineering (ESE). The source material of the review consisted of four state-of-the-art approaches to environmental systems engineering in water and waste-water systems and air pollution dating back to the time period between 2004 and 2010. Apart from an analysis of related work, this review provided an overview of applied research methods and topics in the field of ESE as well as a categorization of the analyzed source material. The environmental systems, such as water cycles, air pollution and ecosystem cycles, are quite complex and known to be highly dynamic, with an uncertainty compared to chemical systems. The challenges associated with the modeling, control and optimization of environmental systems have provided us with fascinating opportunities. Furthermore, elements of a research agenda were discussed in order to determine possible further research steps in the discipline of ESE. Like most other systematic literature reviews, this paper is aimed at an audience of experienced researchers in the field of ESE who are looking for research ideas and junior scientists entering into the emerging field of research. Practitioners might benefit from our review by gaining a stronger awareness of the importance of ESE for business companies. Key words: Water and waste-water systems, ecosystem cycles, air pollution, research agenda. INTRODUCTION Process systems engineering (PSE) in chemical engineering has been known as a well-established technology for the design, control and optimization of chemical systems since 1950s (Klatt and Marquardt, 2007; Gani and Grossmann, 2007). There have been tremendous research on PSE which has been successfully developed and applied in various chemical, petochemical, biochemical and oil industries. Recently, there have been many reports of the new trends and challenges of PSE (Grossmann and Westerberg, 2000; Klatt and Marquardt, 2007). Grossmann and Westerberg (2000) previously discussed the future trends of PSE related to chemical-based products, energy, biosystems engineering and enterpise-wide optimization. Gani and Grossmann (2007) reported the next challenges of PSE, which are multisale model, mixture models, energy and
*Corresponding author. E-mail:
[email protected]. Tel: +82-31-201-2124.Fax: +82-31-204-8114.
sustainability, climate change issues, CO2 storage and environments. Klatt and Marquardt (2007) reported several perspectives on PSE from academia and industrial perspectives. As mentioned by Gani and Grossmannn (2007), the areas of environment and sustainability clearly provide new challenges and opportunies to the CAPE/PSE community. This paper focuses on new challenges and opportunites of PSE to environmental systems engineering (ESE), since the environment is very import-ant as the effective use of resources such as water are paramount, and the negative impact to the air quality is minimized. The natural systems in Figure 1 can be modeled by describing the interactions between the sys-tem components by means of dynamic (time-varying) mathematical equations (Barnsley, 2006). For example, based on the dynamic modeling of natural environmental systems, systems engineering techniques such systems analysis, control and optimization tools can be applied to minimize the pollution or eco-toxicological effect resulting in a
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Figure 1. Dynamic modeling of natural systems by describing the interactions between the system components.
healthy environment. Nielsen (2001) reported that a systematic technology of instrumentation, control and automation can reduce the cost of water treatment process by 20% and can also increase the capacity by 10 - 30% in a biological nurtient-removing wastewater treatment plant by introduction of ingegrated controls. THE NATURE OF ENGINEERING (ESE)
ENVIRONMENTAL
SYSTEMS
Environmental systems engineering is defined here as complex models that represent mathematical, data-driven and biotic structures in combination with physical, biological and ecological processes in water, rivers, air and ecosystems. ESE has a relatively short history of development, and has been applied to various environmental systems. For example, in one ESE, environmental informatics can be considered as the science and art of turning environmental data into information and understanding. A more structural definition is given by the Natural
Environment Research Council; as research and system development which focuses on the environmental sciences relating to the creation, collection, storage, processing, modelling, interpretation, display and dissemination of data and information. This assimilates expertise and technologies and promotes interaction between fields such as environmental monitoring, environmental databases and information systems, geographical information systems, numerical simulation modelling, knowledgebased systems, Internet exploitation, data visualisation, human-computer interaction, information theory and public understanding of science (The Centre for Ecology and Hydrology). Figure 2 shows a scheme of environmental systems engineering. Environmental systems engineering emphasizes the application of optimization, economics and systems engineering to problems in environmental resource management. (Willis and Finney, 2004). ESE brings the two engineering desciplines of environment and systems engineering together to devise and implement solutions which manage interrelated elements of the environment, industry and society, to work towards
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Figure 2. Schematics of environmental systems engineering for environmental resource management.
the goal of sustainable development. To show new opportunities of PSE research topics, four state-of-the-art approaches to ESE in water and wastewater systems, air pollution and their implementation issues were reviewed in this paper. Also, the study’s experiences of the optimization of wastewater treatment, cooling plants and integrated management of air pollution systems were reviewed as well. A STATE-OF-THE-ART APPROACH TO ESE IN OPTIMIZATION OF WASTEWATER TREATMENT PLANTS Due to increasing environmental constraints and the necessity of reliable wastewater treatment, efficient modeling and monitoring methods are becoming more and more important. Reliable modeling and monitoring techniques of biological wastewater treatment plants (WWTP) are necessary to maintain a system performance as close as possible to optimal conditions. The underlying point is that the improving system operation performances, necessarily means ensuring more accurate knowledge of the process. Mathematical modeling and simulation are excellent tools to link information from microscopic and macroscopic scales, short and long time frames, and fundamental and practical knowledge. Therefore, they can provide the knowledge management tools needed to effectively incorporate novel findings into the practical process of designing and operating advanced reactor systems for biological wastewater treatment (Wilderer et al., 2002). The ultimate aim of process engineering is to solve the problem of environmental pollution by optimizing existing processes with minimal investments. For this solution, we applied environmental system engineering to monitor a wastewater treatment plant,
process identification control and find optimal operational conditions for nitrogen and phosphorus (N, P) removal in a biological nutrient removal process (Yoo et al., 2004; Kim and Yoo, 2009; Kim et al., 2009; Yoo et al., 2009). The ultimate objective of this state-of-the-art approach was to suggest an integrated framework of modeling, process monitoring, control and optimization for a sustainable biological wastewater treatment operation. The framework starts to monitor a wastewater treatment process using a multivariate statistical monitoring method. Under the proposed approach, process information obtained from statistical monitoring techniques was utilized to monitor the biological treatment process, to monitor a microbial population dynamics, to design the supervisory control and finally, to optimize the operating conditions. Ultimately, a state-of-the-art approach to provide an integrated framework was attempted. When applied to a pilot-scale sequencing batch reactor, the power and advantages of the proposed method were shown. The following example provides a chance to introduce an experience and opportunity of PSE to environmental systems. Integrated framework of modeling, process monitoring, control and optimization for a sustainable biological wastewater treatment operation The ultimate objective of the example was to suggest the integrated framework of modeling, process monitoring, control and optimization for a sustainable biological wastewater treatment operation, such as an activated sludge process (ASP) and sequencing batch reactor (SBR). Under the proposed approach, process information obtained from statistical monitoring techniques was utilized to monitor the biological treatment process, to
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Macroscopic ON-LINE MONITORING
•on-line fault detection •on-line cheap sensors (DO, ORP, pH, flowrate …) •sensor validation (data reconcilation) •diagnosis (multiple models)
PREDICTION
•soft-sensing (prediction) •lab measurement (MLSS, SVI, eff. COD,N,P …) •impact of disturbances (multiple models) •Integration of on-line and off-line data
Microscopic SETTLING MONITORING
MOLECULAR MONITORING
•floc structure •floc size and size distribution (FSD) •image analysis •Integration of on-line, off-line and settling data
•microbial population long-term performance •specific rRNA genes (DGGE, FISH, …) •Integration of on-line, off-line, and genes data
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•disturbance rejection •supervisory control from monitoring and prediction •multiple models •ASM 1, 2d (mechanistic) •Phase scheduling (SBR)
LONG-TERM CONTROL
•sludge population control based on macroscopic and microscopic model (RSM) •refinement of operating policies •valuable model calibration information for ASM 1, 2d
1
Days 1-70
10
100
1000
Particle diameter (micron)
OPTIMIZATION •Sludge population optimization of statistical OED and RSM •Combination of data and mechanistic model-based optimization
Figure 3. Integrated framework of modeling, monitoring, control and optimization for a sustainable biological treatment operation.
monitor a microbial population dynamics, to design the supervisory control, and finally, to optimize the operating conditions. Advanced techniques of data-driven statistical models, image analysis and activated sludge models, ASM, (Henze et al., 2000) were used to detect the faults, to integrate the monitoring and control systems and to develop model-based optimization for sustainable operation. Specially, we developed a new long-term monitoring technique by integrating process engineering data and microbiology tools, which can monitor and may manipulate the various microorganisms’ communities to enrich the organisms’ distribution and maintain uniform sludge properties. Finally, a state-of-the-art approach to provide an integrated framework was attempted. Figure 3 shows the proposed integrated framework of modeling, monitoring, control and optimization for a sustainable biological treatment operation. Multivariate statistical monitoring of non-Gaussian environmental process The proposed framework was applied to a pilot-scale SBR for performance assessment and analysis. First of all, having a process monitoring system of the biological treatment process is very important, because recovery from failures is time-consuming and expensive. Moreover, some changes are not very obvious, are difficult to detect and may even grow gradually until they produce a serious operational problem. Therefore, early fault
detection and isolation in the biological process is very efficient as it allows execution of a corrective action well before a dangerous situation happens. A monitoring system for abnormalities is of primary concern for supervisory control and optimization. To accomplish this task, we developed a reliable intelligent monitoring procedure for SBR (Yoo et al., 2004), SBR for biological wastewater treatment, is characterized by a variety of error sources with non-Gaussian characteristics. The SBR poses an interesting challenge from the point of process monitoring, characterized by non-stationary, batchwise, multiscale and non-Gaussian characteristics. Obviously, the non-Gaussianity deteriorates the reliability of the multivariate monitoring system, and subjects it to unfavorable criticism. Figure 4 shows the statistical monitoring charts and loading plot of SBR batches using the Multiway principal component analysis (MPCA) and the Multiway independent component analysis (MICA). They represent the loading plot, to show how the variables of SBR are interrelated, and the monitoring charts, to analyze the historical batches, detect and diagnose the abnormal batches. The second stage considered the fine classification of process as faulty in diagnosing the source causes of abnormal process operations. The third aspect of this research was to integrate the process monitoring and control systems, which will enable the determination of the optimal operating conditions from information on the current state of a reactor. A method for automatically determining the optimal operating conditions was
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(b)
Figure 4. (a) T2 and SPE plot of SBR batches using the MPCA, (b) Loading plot of the MICA (Yoo et al., 2004).
developed by combining the data-driven statistical monitoring information and the mixture model of the clustering method. Information on the fault or disturbance derived from the process monitoring techniques was used to design the supervisory control law for the current operating condition. The basic concept underpinning the proposed approach was that different operational regions occupy different regions in the multivariate space; whereas, several methods have been tested in a SBR. On the other hand, novel molecular microbiology methods, such as DGGE and FISH, have led to new insights into microscopic structures, which allow direct incorporation of their formation and describes the metabolic potential of the microorganisms on a specific level as well as on the community level. Transformation of the results obtained on the microscale, required a more complete correlation with the process data, describing the treatment system from which samples were taken. The on-line measured data, integrated with off-line chemical analysis (e.g. PHA, glycogen), and ideally, quantitative microbial population data, could lead to the simultaneous estimation of the stoichiometric and kinetic parameters of both groups of bacteria. Since the interactions between the various processes and bacterial groups are so complex, they cannot only be evaluated fully through the use of integrated mathematical models. This is therefore, a powerful tool in bring together biochemical engineering and microbial data in a way that increases the overall understanding of these complex processes (Keller et al., 2002). For this purpose, we developed a new long-term monitoring technique by integrating on-line measured data, off-line analysis of the nutrient concentrations and the quantitative microbial population data. Figure 5 shows the clustering result by integration of on-line, off-line and microbial gene data for macroscopic and microscopic
monitoring, which includes molecular monitoring for microbial community change via specific rRNA genes (DGGE) and image analysis. This was the first study used in integrating the process of engineering data and microbiology tools, in which they were explicitly taken into consideration, and where the impact of the control actions on the microbial community in the biological system and on the microbiological properties can be analyzed and interpreted. It can monitor and may manipulate the various microorganism communities to enrich the organisms’ distribution and maintain uniform sludge properties (sludge population optimization). Robust model-based optimization of wastewater treatment plants necessitates successful calibration of complex wastewater treatment plant models. The word 'successful' relates to the prediction capability of activated sludge models under variable process conditions, where the model should describe the plant behavior within realistic margins, taking into account the uncertainties of the inputs and reactions taking place in the system. Cost reduction and the search for operating conditions that allow the achievement of appropriate effluent quality criteria (Nitrogen, Phosphate) are the most common targets that are achieved by the model studies. Next to the model selection task, the most important issue is using this model to characterize the overall activated sludge plant behavior, including the biological and physio-chemical phenomena (e.g. settling), the so-called model 'calibration' (Sin, 2004). After successful validation of the model, it can serve for this purpose. For the activated sludge modelers, the calibration protocol tried to combine and link state of the art methodologies for calibration of different processes in a wastewater treatment plant (hydraulics, biological reactions, sedimentation processes, etc.) and it can thus be used as a complete guideline for extensive model
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Figure 5. Clustering result of integration of on-line, off-line and microbial gene data for macroscopic and microscopic monitoring.
calibration tasks. For the model-based optimization, we developed a systematic calibration and optimization approach to determine the best operation strategy for the optimization of the N and P removal performance for the SBR technology. A step-wise calibration protocol used to calibrate the activated sludge model (ASM) and the optimization protocol based on running a grid of scenarios using a calibrated ASM model is tested (Vanrolleghem et al., 2003; Sin, 2004). Optimal criteria of effluent quality and robustness, optimal scenario of four alternating aerobic and anoxic phases with step-feed were selected and were implemented into the SBR system beginning from on the 18th of December 2003. The optimal scenario in Figure 6 provided better results regarding nutrient removal (phosphorous and nitrogen). The switch in operation can easily be seen from the measurements; however, the nitrogen removal level is higher than legal criteria required, while phosphorous criteria are violated. In terms of effluent quality, some of the nitrogen removal capacity should be used for phosphorous removal. The changes in the environmental conditions, with the implementation of the new best scenario, resulted in a changed microbial population. In this research, the integrated framework of datadriven statistical model, activated sludge models and molecular microbiology information were developed to
detect the faults, to integrate the monitoring and control systems, and to develop model-based optimization for sustainable operation. Advanced techniques of datadriven statistical models, image analysis and activated sludge models (ASM) were used to detect the faults, integrate the monitoring and control systems and to develop model-based optimization for sustainable operations. Specifically, a new long-term monitoring technique integrating process engineering data and microbiology tool were developed, which can monitor and possibly manipulate the various microorganism communities to enrich the organisms’ distribution and maintain uniform sludge properties. Its application to a pilot-scale SBR showed the power and advantages of the integrated framework. Moreover, it can be easily applied to a modular internetbased remote supervision and supervisory control system of other wastewater treatment plants, and even to other environmental plants, such as air emission, solid waste and ecological processes. A STATE-OF-THE-ART APPROACH TO ESE IN OPTIMIZATION OF EFFLUENT COOLING SYSTEMS While chemical, physical or biological treatment processes can be used for controlling the chemical pollution problems of effluents, the thermal treatment system is
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Concentration
Ntot (mg N/l)
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PO4P (mg PO4P/l)
30.00 20.00 10.00 0.00 27-Nov 04-Dec 11-Dec 18-Dec 25-Dec 01-Jan 08-Jan 15-Jan 22-Jan 29-Jan 05-Feb Time (day)
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40 30 20 10 0 27Nov
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Time (day)
Figure 6. The concentration change of COD and total nitrogen and phosphorus before and after implementing optimal scenario.
E1
E1 E2
CT
E3 a) Centralized cooling system
E2
CT
E3 b) Distributed cooling system
Figure 7. Centralized and distributed effluent cooling systems.
required for effluent temperature reduction problems when the temperature of the effluent streams is too high to be discharged directly to the receiving water. It has been used to reduce the temperature of effluents that are diluted with regional water (river, lake, estuaries or coastal water) close to industrial sites and discharged to the environment. However, this practice is not a longterm solution and is restricted by government authorities for ground/surface water protection (Ataei et al, 2009a; Kim et al., 2001). Therefore, the introduction of cooling systems is inevitable for solving effluent temperature reduction problems. Effluent temperature reduction can be accomplished by simply installing cooling equipment before discharge
(centralized cooling). However, this can be expensive and the design of effluent treatment systems should be based on an investigation of the whole process. As a centralized cooling policy (Figure 7a) cannot avoid the degradation caused by mixing effluents with low temperatures, a distributed cooling policy (Figure 7b) for cooling systems should be considered (Ataei, 2010). The new Optimum Design method of distributed Effluent Cooling system, which is called ODEC, for costefficient effluent temperature reduction was achieved using a systematic approach (Ataei et al., 2009a). With the ODEC, the optimum distributed effluent system is designed in five stages (Figure 8). The first stage is the construction of the effluent composite curve (Figure 9).
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Targeting Procedure
Stage 1
Stage 2
Stage 3
Design Procedure
Stage 4
Stage 5
Figure 8. Targeting and design procedures in the ODEC (Ataei et al., 2009a).
conditions. The operating cost and the capital cost of the cooling tower have different effects on the overall cost of the distributed cooling system. Therefore, the problem of targeting the distributed cooling system becomes an optimization problem, to find the optimal cooling line. The total cost of the cooling tower, as the objective function, is expressed in the following equation (Ataei et al., 2009b; Ataei et al., 2008):
TC= Ci
Ai + Ry
CelecEf ma3Ry2Z2hrs(6.5 + Kel + 2( 2ρa Ai2ηfanηm
Ai 2 )) RyZAfan
+ Aic
(1)
Cooling tower design model In a counter-flow wet cooling tower, the process consists of a gas phase (air) flowing upward and a liquid phase (water) flowing downwards, and a large interface between these two phases. It has been noted that the rate of energy transferred from the water is equal to the rate of energy gained by air (Equation 2) (Ataei et al., 2009b): Figure 9. Construction of the effluent profile composite curve (Ataei et al., 2009a).
Qa = ma (ha ,out − ha ,in )
(2)
The air flow rate of the tower can be achieved with a known water flow rate as calculated in Equation 3 (Ataei et al., 2008):
ma =
mw C pw C pa
(3)
The change in the air humidity ratio along the cooling tower and the saturated humidity ratio at water temperature were given in Equations 4 and 5 (Ataei et al., 2008):
Figure 10. Feasible boundaries for the distributed cooling system.
The second stage is the generation of the feasible region, taking into consideration the system limitations (Figure 10) (Ataei et al., 2009a). The third stage targets the optimum cooling tower supply line by exploring the feasible area. The fourth stage is the design of the cooling network to achieve the target, based on the modified grouping rules and the final stage is the design of the cooling tower to achieve the targeted total cost of the cooling tower, considering targeted temperatures and flow rates of inlet and outlet
hd A fr dTw = (Tw − Ti ) dz mw C pw
(4)
ha A fr dTa = (Ti − Ta ) dz ma C pa
(5)
For the air-water system, heat and mass transfer coefficients are represented as a function of air and water flow rates. The related coefficients are given in following Equations (Ataei et al., 2009b):
K a A fr = a1 m ab1 m wc1
(6)
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Table 1. Effluent stream data.
Effluent 1 2 3
Z
Flow rate (kg/s) 83.33 83.33 111.11
Temperature (°C) 60 45 32
ma
ha1,out Z = Z + dZ
Ta ,out Ta ,out
ha 2,out
ha1,out − ha 2,out ≤ ε
T w,out − Tw,out ,Calc ≤ ε
Acr
dT Ti , w , Tw, out dz
Figure 11. Flowchart of optimum cooling tower design
hd A fr = a2 mab 2 mwc 2
(7) Figure 12. Overall cost of the distributed cooling system.
ha A fr = a 3 m m b3 a
c3 w
(8)
The optimum heat and mass transfer area can be calculated as shown in Equation 9 (Ataei et al., 2009b):
Ai ,opt = 3
Celec E f ma3 Ry 3 Z 2 S [6.5 + K el + 2( A fr / A fan ) 2 ]
ρ a 2η fanη motor Ci
(9)
The optimum cross sectional area is given in Equation 10 (Ataei et al., 2009a):
ACr ,opt =
Ai ,opt RyZ
(10)
It is assumed that the cooling tower frontal area and cross-sectional area will be approximately equal. If the design is for a rectangular cooling tower, the frontal area is given in Equation 11 (Ataei et al., 2009a):
ACr ≈ A fr = Z × W
(11)
To achieve the optimum cooling tower design, an iterative calculation is required. The computation procedure is presented in Figure 11. Illustrative example The effluent streams data in Table 1 was examined as an
illustrative example for optimum design of effluent cooling system, using the proposed design method (ODEC). The following parameters were used for the illustrative example: The electricity cost is 0.08 $/kWh. The eliminator –1 characteristic is 2.8 × 105 m . The eliminator friction coefficient is 4.6. The operating period is 8600 h/yearr. The environmental temperature discharge limit is 30°C. The wet bulb temperature is 20°C and minimum approach temperature is 5°C. Figure 12 illustrates the effect of the water flow rate on the capital and operation costs of the distributed cooling system. The results reveal that an increase in the water flow rate reduces the cooling tower capital cost, whereas the operating cost increases. Therefore, a trade-off between the capital cost and the energy cost has been introduced. The optimum water flow rate is achieved by considering this trade-off. The optimum cooling line, which is located between the maximum and minimum flow rates, achieves the minimum total cost. Table 2 shows design parameters and cost comparison of ODEC and those of the conventional design (centralized cooling system). To achieve the targeted cooling tower supply flow rate, the effluent 1 and 2 should be passed through the cooling tower totally and the effluent 3 should be partially cooled and partially bypassed. Figure 13 shows the optimum
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Table 2. Results comparison of conventional design method and ODEC.
Design method
Conventional ODEC
Flow rate (kg/s) 277.77 169.00
Inlet temperature (°C) 44.31 51.07
Outlet temperature(°C) 30 27.55
Operating cost (k$/year) 45.52 31.51
Capital cost (k$/year) 41.79 30.31
Total cost (k$/year) 87.31 61.82
Figure 13. Optimum effluent network of the ODEC.
effluent network produced with the ODEC on the basis of the modified grouping design rules. As seen, a cost-effective design method for a distributed effluent cooling system has been introduced to cope with problems of thermal pollution. The results indicate that, by distributing the cooling system, the effluent streams are partially treated thermally by the cooling tower. Therefore, the required cooling tower in the distributed system is smaller than that in the centralized system. Hence, applying the ODEC has resulted in cost minimization relative to that of the conventional (centralized cooling system) design method. Table 2 shows how 25.5k$/year can be saved by applying the ODEC on the illustrative example. A STATE-OF-THE-ART APPROACH TO ESE IN OPTIMIZATION OF RE-CIRCULATING COOLING WATER SYSTEMS Researches on Re-circulating Cooling Water Systems (RCWSs) have mostly been focused on the cooling system components individually, not the system as a whole (Ataei et al., 2010; Panjeshahi et al., 2009; Panjeshahi and Ataei, 2008). However, a simultaneous integration of RCWS components provides opportunity to achieve the optimum design. Kim and Smith (2001) presented a systematic design methodology of RCWS (KSD method), which accounts for the interactions between the
cooling tower and heat-exchanger network. The KSD methodology allowed minimum water flow rate to participate in the performance parameters calculation and network configuration design, considering fix approach value, as the cooling tower design variable. However the minimum flow rate through the fix approach value does not necessarily ensure the optimum value (Ataei et al., 2009c). In this state-of-the-art approach, the KSD method was modified and expanded to achieve minimum total annual cost. The modifications were done in two stages. At the first stage, a new grass root design method is introduced. This new design methodology, which we called Advanced Pinch Design method (APD), was based on combined pinch analysis technology and mathematical programming (Ataei et al., 2009c). At the second stage, the APD method was improved by introducing regeneration recycling opportunity via air cooler in heat exchanger network. Therefore, the APD with Regeneration Recycling (APDRR) design method was developed for conservational opportunity in addition to the minimum cost achievement. Finally, the results of the introduced grass root design methodologies, (APD and APDRR) were compared with KSD. The objective function of the grass root design methodologies was the total annual cost of cooling tower, which included operation and capital cost. The total cost of cooling tower in K$/yr is calculated as shown in Equation12 (Ataei et al., 2009c):
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Figure 14. Temperature feasible region.
Table 3. Hot process stream data.
Streams
1 2 3 4 5
Thot,in (ºC) 50 55 70 70 75
Thot,out (ºC) 40 45 45 65 70
CP (KW/ºC) 40 100 50 100 50
Q (KW) 400 1000 1250 500 250
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examined. The optimization was made using MATLAB version 7.3. In this example, five hot process streams data were assumed according to Table 3. The performance parameters of APD and APDRR were compared with KSD method, which is shown in Table 4. The heat exchanger configuration of the KSD is shown in Figure 15. The results illustrated that the heat exchanger configurations achieved through APD and APDRR grass root design methodologies, were supplied the series arrangement. The series configuration of heat exchanger network in RCWS design provided much opportunity for cooling water network to reuse cooled water between different cooling duties. The heat exchanger configurations of APD and APDRR are shown in Figures 16 and 17, respectively. Note that in o Figures 14 - 17, CP is the thermal stream in kW/ C and T is the temperature in degree centigrade. Results on the presented illustrative example showed that applying the APD and APDRR design methods result in an achievable total cost of 32.02 K$/year and 30.84 K$/year, whereas, the total cost of KSD is 34.19 K$/year. Therefore, APDRR resulted in minimum cost achievement in comparison to the other design methodo-logies. In addition, the result of the analyzed example demonstrated 17% energy saving for APD, 72% water saving and 71% energy conservation for APDRR in comparison with KSD. A STATE-OF-THE-ART SYSTEM MANAGEMENT OF INDOOR AIR QUALITY IN A SUBWAY STATION Introduction
TC = 671.941( Fin ) 0.79 ( R ) 0.57 ( A) −0.9924 (0.022TWB + 0.39) 2.441 44( Fair ) + 131.639( Fin ) + 227.513( M ) + 1138( B ) (12) where, the Fin is the cooling water flow rate in tons per hour. The A, R and TWB are the approach, range and the wet bulb temperature, respectively in degree centigrade. The Fair is the air flow-rate in tons per hour and the M and B are the make-up and the blow-down flow rates, respectively in tons per hour. The operating and capital costs of the cooling tower have different effects on the overall cost of RCWSs. The minimum cost can be found through the optimum water flow rate. To target the optimum water flow rate, the feasible region should be introduced. The feasible area (Figure 14) is defined considering the pinch technology and the constraints that are dictated through the RCWS. Finally, the heat exchanger configuration can be achieved through an advanced synthesis algorithm (Ataei et al., 2009d; Ataei at al., 2009e; Ataei and Yoo, 2010). To investigate the effect of the presented design methods, APD and APDRR, on the performance parameters and total cost, an illustrative example was
Metro systems, or underground or subway systems, have been considered an important model of transport in order to increase the quality of transport, relieve congestion and fill gaps of insufficient public transport (Paivi et al., 2005). Korea’s Ministry of Environment (MOE) established the indoor air quality (IAQ) act to control major pollutants such as PM10, CO2, CO, VOC and formaldehyde in indoor environments like subway platforms (Kim et al., 1994). There are two subway systems in Seoul, where the Seoul Metro Subway Corporation (SMSC) operates subway lines (1 - 4) and the Seoul Metropolitan Rapid Transit Corporation operates subway lines (5 - 8). We examined air pollutant data from a real-time tele-monitoring system (TMS) installed in four subway stations located on subway line numbers 1, 2 and 4 in Seoul. The TMS systems in Figure 18(a) are located at the center of the platform in each station and measure the concentration levels of seven air pollutants: NO, NO2, NOX, PM10, PM2.5, CO, CO2, and temperature and humidity with fixed measurement intervals. Although several peaks are observed in Figure 18(b), the concentrations of particulate matter (PM10 and PM2.5) are all technically within the controlled state. However, the correlations between air pollutant variables are not considered in
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Table 4. Performance parameters comparison with KSD.
Design methods KSD APD APDRR
Thot,in (ºC) 65.15 60.18 60
Tcold,out (ºC) 30 25.77 25.77
CP ( KW/ºC) 130 98.81 28.11
F (t/h) 111.96 85.1 24.21
Figure 15. Heat exchanger configuration by KSD method.
Multivariate statistical monitoring of indoor air quality
Figure 16. Heat exchanger configuration by APD.
univariate models. Thus, simultaneous examination of all seven air pollutants and two environmental variables (temperature and humidity) are instructive (Kim et al., 2010). We developed an integrated management system of indoor air quality (IAQ) which applied the process systems engineering technologies to an environmental system. Figure 19 shows the integrated scheme of the multivariate monitoring, diagnosis and control for indoor air quality management. First, multivariate monitoring based on principal component analysis (PCA) was used to diagnose the nine air pollutants in a tele-monitoring system (TMS) simultaneously in the viewpoint of ecological toxicology. Second, a contribution plot was used to identify and isolate the sources of bad, contaminated or emergency air quality. Third, under the process information obtained from statistical monitoring techniques, the artificial intelligence (AI) of such a recurrent neural network, Fuzzy, can be utilized to design the supervisory control algorithm, and finally, to optimize the operating conditions. Here, the integrated monitoring of air quality was accomplished by combining multivariate monitoring and the prediction method to check their toxicological health effects using a statistical regression.
It is important to assess the overall health risks posed by all of the air pollutants found in an environment simultaneously. Multivariate monitoring methods that consider all available data simultaneously can extract key information about the relationships and combined effects of air pollutants. The TMS data were taken every minute in day, creating a database with 365 samples. Since univariate indices and multiscatter plots are not suitable for showing relationships between objects and variables, we used a multivariate statistical method using PCA to simultaneously consider nine air quality variables. Daily average value of TMS dataset was used to monitor the indoor air quality with a multivariate statistical method of PCA. For the PCA, daily average value was used. The score plot shown in Figure 16 reveals the model and the degree of variation in each piece of data, as it represents 2 about two-thirds of the total variation. The Hotelling’s T statistics is displayed as a 95% tolerance region. The four classes in Figure 16 represent the seasonal change of nine air pollutants, where most of the data in summer and winter existed on the second quadrant and fourth quadrant, respectively, located on the opposite trend. Some measurements outside of the tolerance region were considered as abnormally polluted samples. This difference was revealed with PCA, which was undetected by the univariate plot because the combination of nine variables in one single scale resulted in a severe loss of information. To understand and interpret the distribution of the samples in Figure 20, it is instructive to examine the corresponding loading plot shown in Figure 20(b). The loading plot gives an overall picture of the variable correlation structure. The nine variables were grouped in three clusters. The first cluster relates to temperature and
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Figure 17. Heat exchanger configuration by APDRR.
Figure 18. (a) TMS at B-subway station used in this study, (b) Univariate monitoring results of the air pollutants of PM10 and PM2.5.
Figure 19. Integrated framework of multivariate monitoring, diagnosis and supervisory control of air pollutants in a subway station.
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b Figure 20. (a) Score plot of the PCA model, (b) Loading plot.
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Figure 21. (Left) Sensor fault detection of PM10 sensor and identification bias (a) SPE plot, (b) FSR, (c) IFSR, (d) CVI, (Right) Sensor reconstruction of PM10 sensor bias (a) normal, faulty and reconstructed signals, (b) fault size.
humidity, the second cluster relates to PM10 and PM2.5 and the last cluster relates to NO, NOx and CO2. Thus, sample (70302) is strongly related to the last cluster, and another sample (70912), is related to the second cluster. A dynamic process monitoring system based on enhanced sensor reliability for sustainable system monitoring The efficient implementation of sensors and instrumentation devices is a prerequisite for the successful application of any process monitoring or control system tasks. However, environmental plants are notorious for poor data quality and sensor reliability problems, due to the hostile environment, missing data problems and more. In spite of sensor failures, the monitoring system should be fully operational; this requires a robust and reliable monitoring scheme. To ensure correct operation of control systems, the measurement and control equipment in plants must be mutually consistent. For monitoring quality standards, a high degree of accuracy is needed, but only low demands are set on the timescale. In control applications, on the other hand, a high measuring frequency and a short response time are essential (Rieger et al., 2003; Yoo et al., 2006). Hence, the accuracy of sensors is crucial to successful process control and monitoring, and the ability to detect sensor faults is very useful, especially in processes that are monitored and controlled based on process information from many sensors. Faulty sensors that are either completely or partially failing (hard fault or soft fault; for example, bias, drift or precision degradation of a sensor signal) provide incorrect information for monitoring and control. Therefore, prompt detection of the occurrence and correct identification of the location of sensor faults and
reliable reconstruction (or recovery) of faulty sensors is of primary importance for efficient operation. In this research, a sensor reconciliation method using maximum sensitivity based on the redundancy of the measurements was used to detect, identify and reconstruct faulty sensors in a biological process. In this research, we used a structured residual approach with maximized sensitivity (SRAMS) for the detection and identification of faulty sensors using a normal, quasi-steady state process model suggested by Qin and Li (2001). We built a sensor fault identification model using PCA with three principal components, and designed a matrix W with SRAMS. Filtered SPE and FSR were used to detect the sensor faults and two indices of IFSR and cumulative variance index (CVI) with the 95% confidence level were monitored to identify faulty sensors. Four types of sensor faults, including bias, drift, complete failure and precision degradation, were introduced at time tf, where the abnormal condition was caused by single and multiple sensor failures. The remaining measurements were used to reconstruct the faulty sensor based on the redundancy of the measurements. The fault shown in Figure 21 was generated by introducing a drift into the sensor measurements of PM10 at tf = 200. Good fault identification results were obtained for this sensor fault. The reconstructed sensor signal indicated that the difference between the normal and the reconstructed sensor trajectory was relatively large with an increasing offset. The estimated fault size indicated that this is a drift fault, causing FSR to be more effective than CVI. Supervisory control based on the knowledge transfer from the monitoring to the control system Figure 22 shows the concept of processing information of PCA with air pollutants in a subway station and a super-
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Figure 22. (a) Concept of processing information of PCA with air pollutants in a subway station, (b) supervisory control based on the knowledge transfer from the monitoring to the control system.
visory control based on the knowledge transfer from the monitoring to the control system. Under the proposed approach, data obtained from the TMS-monitoring system is utilized in detail to monitor the indoor air quality in a subway station using PCA. Next, this monitoring information, such as normal, contaminated or toxic air quality conditions, can be transferred to design the supervisory control, and finally, to optimize the ventilation conditions. A state-of-the-art approach to provide an integrated framework is an on-going research topic, which is based on monitoring, supervisory control and optimization for indoor air quality management. We demonstrated the feasibilty of integrated framework of indoor air quality control system using the PSE technologies in subway stations. CONCLUSION This paper represented a review in the field of Environmental Systems Engineering (ESE) and provided new opportunities in process systems engineering (PSE) for researchers that are related to environmental and energy systems. To show new opportunities of PSE research topics, four state-of-the-art-approaches to optimization of wastewater treatment, cooling systems and air pollution, and their implementation issues were reviewed. As shown, the area of PSE can be extended to environmental systems and used to develop sustainable management with new systematic technologies and tools to solve problems; that is, ESE is an emerging research area. Thus, the goal of this paper was to provide a basic
introduction to the emerging concepts of ESE as new challenges and opportunities for PSE researchers. ACKNOWLEDGEMENTS This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korean government (MEST) (KRF-2009-0076129) and Seoul R&BD Program (CS070160). NOMENCLATURE a1,2,3, b1,2,3, c1,2,3, Constant value of mass transfer coefficient; A, approach temperature, ˚C; Acr, cross 2 2 section area, m ; Afan, fan casing area, m ; Afr, tower 2 2 frontal area, m ; Ai, heat and mass transfer area, m ; Aic, area-independent initial cost, $; B, cooling tower blow down, t/h; Celec, electricity cost, $/kWh; Ci, initial cost of 3 tower per unit volume, $/m ; Cp, Specific heat of water at constant pressure, kJ/kg°C; Ef, economic factor; Fair, Air flow rate, t/h; Fin, Cooling water flow rate, t/h; ha, heat 2 transfer coefficient of air, kW/m °C; hd, heat transfer 2 coefficient of water, kW/m °C; hrs, Annual operating 3 hours, h/yr; Ka, tower characteristic, kg/m s; Kel, eliminator coefficient; M, cooling tower make up, t/h; m, flow rate, kg/s; Q, heat transfer rate, kW; R, Range –1 Temperature, ˚C; Ry, eliminator characteristic, m ; T, temperature, °C; TC, total cost, $/yr; TWB, Ambient wet bulb temperature, °C; W, cooling tower width, m; Z, cooling tower height, m;
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η, Efficiency; , density, kg/m ; Subscripts a, Air; cold, cold stream; fan, fan; hot, hot stream; i, interface; in, inlet; m, motor; opt, optimum; out, outlet; w, water. REFERENCES Ataei A (2010). Wastewater Treatment:Energy-Conservation Opportunities. Chem. Eng-New York, 117(1): 34-41. Ataei A, Gharaie M, Parand R, Panjeshahi E (2010). Application of Ozone Treatment and Pinch Technology in Cooling Water Systems Design for Water and Energy Conservation. Int. J. Energy Res., 34: 494-506. Ataei A, Panjeshahi MH, Gharaie M, Tahouni N (2009a). New Method for Designing an Optimum Distributed Cooling System For Effluent Thermal Treatment. Int. J. Environ. Res., 3(2): 155-166. Ataei A, Panjeshahi MH, Parand R, Tahouni N (2009c). Application of an Optimum Design of Cooling Water System by Regeneration Concept and Pinch Technology for Water and Energy Conservation. J. Appl. Sci., 9: 1847-1858. Ataei A, Panjeshahi MH, Gharaie M (2008). Performance Evaluation of Counter-Flow Wet Cooling Towers Using Exergetic Analysis, T. Can. Soc., 32(3-4): 499-511. Ataei A, Panjeshahi MH, Gharaie M (2009b). A New Algorithm for Optimum Design of Mechanical Draft Wet Cooling Towers, J. Appl. Sci., 9(3): 561-566. Ataei A, Panjeshahi MH, Gharaie M (2009d). New Method for Industrial Water Reuse and Energy Minimization, Int. J. Environ. Res., 3(2): 289-300. Ataei A, Panjeshahi MH, Karbassian S (2009e). Simultaneous Energy and Water Minimization-Approach for Systems with Optimum Regeneration of Wastewater, Res. J. Environ. Sci., 3:604-618. Ataei A, Yoo CK (2010). Simultaneous Energy and Water optimization in Multiple-Contaminant Systems with Flowrate Changes Consideration. Int. J. Environ. Res., 4(1):11-26. Barnsley MJ (2006). Environmental Modeling A Practical Guide. CRC press. Gani R Grossmann IE (2007). Process Systems Engineering and CAPE – What Next ?, 17th European Symposium on Computer Aided Process Engineering 1-5. Grossmann IE, Westerberg AW (2000). Research Challenges in Process Systems Engineerng. AIChE J. 46: 1700-1703. Henze M, Gujer W, Mino T, Loosdrecht M (2000). Activated Sludge Models ASM1, ASM2, ASM2d and ASM3. IWA Scientific and Technical report, IWA: UK. Keller J, Yuan Z., Blackall L (2002). Integrating Process Engineering and Microbiology Tools to Advance Activated Sludge Wastewater Treatment Research and Development, Reviews in Eng. Sci. & BioTech., 1:83-97. Kim MH, Rao AS, Yoo CK (2009). Dual Optimization Strategy for N and P Removal in a Biological Wastewater Treatment Plant, Ind. Eng. Chem. Res., 48(13): 6363–6371. Kim YS, Kim JT, Kim IW, Kim JC, Yoo CK (2010). Multivariate Monitoring and Local Interpretation of Indoor Air Quality in Seoul’s Metro System, Environ. Eng. Sci., (In Press). Kim JK, Savulescu L, Smith R (2001). Design of Cooling Systems for Effluent Temperature Reduction. Chem. Eng. Sci., 56:1811-1830. Kim JK, Smith R (2001). Cooling water system design.Chem. Eng. Sci., 56:3641-3658. Kim YS, Sung SW, Yoo CK (2009). On-line process Identification and Implementation of a PID Controller in a Full-scale Wastewater Treatment Plant. Environ. Eng. Sci., 26(11): 1643-1653.
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