Documents
Resources
Learning Center
Upload
Plans & pricing Sign in
Sign Out
Get this document free

Cost Effective and Optimum Strategies for Emission Reduction in

VIEWS: 21 PAGES: 24

									Cost Effective and Optimum Strategies for Emission Reduction in
                             Refineries




                   Sabbatical Leave Proposal




                 Dr. Habib Hussain Al-Ali
           Department of Chemical Engineering
       King Fahd University of Petroleum & Minerals
                      Dhahran 31261
                       Saudi Arabia




                         October 28, 2006




                                  0
Table of Contents

 A. Summary                                               2

  1.   Introduction                                           2
  2.   Relationship to Existing Research and Literature       3
  3.   Project Objectives                                  6
  4.   Research Plan and Methodology                       6
  5.   Relevance to department,university and Kingdom     8
  6.   Publication Plan                                    9
  7.   Suitability of Host Institution                     9
  8.   Budget                                                 9
  9. Conclusion                                           10
  10. References
  11. CURRICULUM VITAE                                    13
  12. Appendicies                                         18
         a. Appendix A                                    18
         b. Appendix B                                    20
         c. Appendix C                                    21




                                         1
                                 Summary


              In this proposed research work, a new model for air pollution
control from refineries in Canada will be described. A decision support
system will be developed that can select the best control strategy for
refineries to achieve a given pollution reduction level. The originality of this
proposal is that it will lead to the development of a unique decision support
tool to determine strategies for pollution reduction in a refinery


1) Introduction
     Petroleum refinery operations range from the receipt and storage of
crude oil at the refinery to petroleum handling and refining operations and,
finally, to storage and shipping of the finished products. A refinery‟s
processing scheme is determined largely by the composition of the crude oil
input and the chosen mix of petroleum products. The mix and arrangement
of refining processes will vary among refineries; few, if any, use all of the
same processes. Crude oil consists of a mixture of hydrocarbon compounds
(such as paraffinic, naphthenic, and aromatic hydrocarbons) and small
amounts of impurities including sulphur, nitrogen, oxygen, and metals.
Separation, conversion, and treating processes are used to produce various
petroleum refinery products. Separation processes such as atmospheric
distillation, vacuum distillation, and gas processing are used to separate
crude oil into its major components. In turn, conversion processes, such as
cracking, visbreaking, polymerization and alkylation, are utilized to produce
high-octane gasoline, jet fuel, and diesel fuel as well as other light fractions
through the conversion of components such as residual oils and light ends.
Last, treating processes stabilize and upgrade petroleum products by
separating them from less desirable ones and by removing unwanted
elements (such as sulphur, nitrogen, and oxygen removed by
hydrodesulphurisation, hydrotreating and chemical sweetening. Other
treating processes include desalting and deasphalting, which are used to
remove salt, minerals, and water from crude oil prior to refining.

             Despite the large number of products produced by the average
refinery, the products that dictate refinery design are relatively few in
number. Basic refinery processes are based on the large-volume products,

                                       2
such as gasoline, jet fuel, and diesel fuel. Economic balances are required to
determine whether certain crude fractions should be sold as is or processed
further into higher value products.

             Air emissions vary significantly among refineries, both in terms
of quantity and type. Factors that affect the amount and type of refinery air
emissions include crude feedstocks, processes, and the age of the refinery.
Air emissions occur from a multitude of sources and usually are handled
separately. Thus, air pollution control systems, when used, are associated
with specific process units and usually are designed to remove or modify
specific contaminants. The major types of air contaminants associated with
refining are sulphur oxides, hydrocarbons, nitrogen oxides, particulate
matter, volatile organic compounds (VOC), and carbon oxides.

      Table 1 shows the emissions from four typical refineries in Canada. It is
clear from the tables that refineries represent major sources of air emissions.
It is therefore imperative to develop strategies to reduce these emissions. We
consider below our vision for a decision support system that will be able to
reduce emissions to the atmosphere when appropriately employed.

    The host institution is University of Waterloo, Chemical engineering
Department, Canada. University of Waterloo has long been recognized as
the most innovative university in Canada. For 13 of the past 14 years,
Waterloo has been ranked most innovative among 47 universities across the
country. In 2005, for the second year in a row, they also ranked best overall.
Environmental technologies research is one of the important areas of
research in University of Waterloo.

   Saudi Arabia is the largest oil producing country in the world. Oil
processing plants, refineries, and petrochemical plants scattered allover the
country and omit huge amount of air pollutants to the atmosphere. The
proposed work model can be applied later on to many of the oil plants in
Saudi Arabia.


2) Relationship to Existing Research and Literature

       Over the past decade, numerous approaches have been proposed for
air pollution abatement planning. These can be classified according to their
spatial and temporal applicability or their use of various techniques.
Guldmann and Shefer (1980) classified these approaches as 1) simulation


                                      3
and input-output approaches, and 2) cost-effective optimization approaches.
These latter approaches can further be subdivided to a) emission-oriented
optimization models, and b) ambient air quality oriented optimization
models. These models in turn comprise the static and dynamic approaches.
Based on the goal of meeting air pollution abatement with air quality
standards, Flagan and Seinfeld (1988) divided the air pollution abatement
into two categories: 1) long-term control, and 2) short-term control. The
elements of long-term control include urban planning, rescheduling of
activities, and programmed reduction in the quality of pollutants emitted.
The elements of the short-term control strategy, on the other hand, involve
rescheduling of activities and immediate reduction in emissions.

       Rossister et al., (1994) and Pirrone and Batterman, (1995) devised
graphical techniques for ranking and selecting various pollution control
options for pollution prevention. Rossiter, et al. (1994) indicated the
advantages of this approach: it provides a clear representation of the relative
costs and emission control benefits of each option and combination of
options. For a case study of radon control in residences, Pirrone and
Batterman (1995) developed cost curves that used one or several control
strategies to achieve a range of concentration reductions. Their ultimate goal
was to find out the preferred strategies at various mitigation levels. A similar
cost-benefit study has been recently conducted by Haq et al. (1997) to
determine the pollution control measures for a case study in the cement
industry. Their analysis was based on a selection of the control equipment,
which complies with the emission regulations at the lowest cost. The main
advantage of the above graphical techniques is their simplicity at arriving at
a solution. However, the solution obtained is usually not optimal. This is
especially the case when the selection problem is highly combinatorial.

       Many authors have applied the tools of systems analysis to the
economic evaluation of environmental problems. The use of various
techniques of systems analysis such as linear and integer programming has
been applied to environmental economic evaluation. Kohn (1968) proposed
a simple linear programming model that can determine a suitable air
pollution control strategy for a given situation. The method was based on the
premise that air quality goals should be achieved at the least possible cost.
Kohn was not concerned with long range trends or technological
development. The main drawback of this model is that it emphasizes only
the percent use of a control strategy and not on the selection problem.
Schweizer (1974) developed a method for the optimal mix of high and low

                                       4
sulfur fuels for power plants so that environmental criteria are met and plant-
operating schedules are preserved. Lou et al. (1995) used linear
programming analysis for the optimal arrangement of pollution control
equipment among various pollution sources. They applied their model to a
steel-making plant, in which 24 particulate pollution sources and four
optional types of control equipment were included. The costs, efficiencies,
emission factors, types and characteristics of control equipment were
examined under various total emission standards. Jackson and Wohlers
(1970) developed a comprehensive model to determine the costs of
controlling air pollution in a metropolitan area. The model was based on: 1)
identification of air pollutants and emission sources, 2) specifying all
feasible control methods for each pollutant, and 3) predicting the
interrelations that can occur in the dynamic setting of the region. The model
permits the determination of the percent use of least cost solutions.

Even though linear programming models are much simpler to solve than
integer programs, the main drawback of these models is that they determine
the percent utilization of a control option only. These models, therefore, do
not solve the selection problem of controls involved in planning for pollution
abatement. Kemner (1979) developed an integer programming model to
select a set of controls that can meet a given emission restriction at the
lowest cost. The model was applied to a coke plant and was solved by trial
and error using plots of the control cost for various desired reductions.
Holnicki (1994) presented a similar model for implementing a pollution
control strategy in regional scale. The approach is related to the optimal
funds allocation for emission reduction in a given set of power station
installations. The model was solved using a heuristic technique that
systematically tries to determine a good (sub-optimal) solution to the control
selection problem. More recently, Shaban et al., 1997 and Elkamel et al.,
1998. Considered emission reductions in a urea plant and also emissions
during oil production operations. Emissions in petroleum refineries will pose
the additioal challenge of considering multiple pollutants at the same time.
We are also considering the integration of CO2 management within the
overall pollution control problem. Furthermore, it is proposed here to
automate the emission reduction process through the use of a decision
support system that includes different building blocks ranging from data
analysis, model formulation, solution approaches, and ranking of control
options.




                                      5
3) Project Objectives

       The objective of this proposal is to develop a decision support system
that can select the best pollution control strategy for refineries to achieve a
given pollution reduction level. The objective is to minimize the pollution
abatement cost. The decision support system takes into account the retrofit
selection case. If new sources are developed, emissions are increased, or
regulations are changed, the system should present the best adaptive strategy
for dealing with these changes. The system should also suggest the
installation of new controls on existing or new pollution sources, or it may
suggest the combination of new sources with old sources under existing
controls. Several pollutants will be considered including particulates, sulfur
dioxide, nitrogen oxides, and hydrocarbons. In addition, CO2 emissions will
also be looked at with the aim of integrating CO2 management within the
general pollution control problem in a refinery.

       The originality of this proposal is that it will lead to the development
of a unique decision support tool to determine strategies for pollution
reduction in a refinery. This system will be taylored to the refineries that
imperial oil operates and will incorporate multi-period planning and allow
for scenario analysis. The optimal strategy will be determined by economic
and market analysis, given a stated set of policy priorities.


4) Research Plan and Methodology


       Consider an existing refinery with a number of existing emission
sources. Each source is emitting a number of pollutants. Assume further that
a number of control strategies has been implemented in the past in order to
meet certain environmental regulations. These strategies have been selected
after a careful understanding of releases to the environment (current and
projected), by identifying various control strategies, by defining reduction
requirements, and finally by using appropriate cost and economic
considerations. Suppose now that we have an expansion in the refinery, an
increase in production, or a change in environmental regulations. These will
cause, respectively, the generation of new sources of pollution, an increase
in emissions to the environment, or violation of environmental standards.
The question becomes: How to supplement the existing controls in order to

                                      6
comply in an economic way? One of the objectives of this proposal is to
derive an optimization model that will react to the above disturbances in the
original pollution data base in a cost effective manner.

       A pollution data base consists of the set of uncontrolled emission rates
for each pollutant, capital and annualized costs, control efficiencies of
various controls, and pollution source characteristics. It is clear that any of
the disturbances mentioned before will alter this data base and will therefore
call for new decisions to be made. In addition, pollution data bases are
usually assembled based on a trend analysis and must be corrected for any
discrepancies in the old forecast.

      In order to comply with environmental standards, a refinery will have
a number of options to choose from. First, it might decide to adapt additional
control measures for the existing pollution sources. It can also opt to install
control units on the new sources, or use economy of scale and combine the
new sources with existing sources under the same control. The latter is not
always feasible due to refinery layout and the location of pollution sources.
If a combination of sources is possible, then piping costs and other
adjustments must be taken into consideration. In addition, there will be a
change in the flue gas flow rate to the control unit and an increase in
operating cost. The design capacity of the pollution control units must also
be adhered to.

       In order to formulate the least-cost retrofit pollution control problem
mathematically, the important decisions and basic parameters of the system
must be put in symbolic form. We will distinguish between two types of
pollution sources: existing and new. The yearly control cost of a new
control units on new sources will be calculated as the sum of the
operational cost and the annual equivalent (capital recovery) of the
investment cost. For new sources that are combined under existing
controls, the cost will include piping costs, adjustments, and changes in
operational costs. The objective of the model will be to select the least-cost
control strategy for the refinery. Decision variables will be defined which
will allow the determination of the optimum pollution strategy for reducing
emissions in the refinery. The decisions will be evaluated against an
objective function that will consist of the costs of new controls installed on
existing sources or on new sources.




                                      7
       An effective way to comply is to consider the overall environmental
effect as the main criteria. Such constraints about the required air quality
standards will be formulated and included in the model. The optimization
model will involve discrete and continuous variables as well as uncertainties.
These kind of models are combinatorial optimization problems that are
difficult to handle. This difficulty is due to the exponential growth of
solution space with a linear increase in the number of variables in the model.
GAMS (Generalized Algebraic Modelling System) is usually employed as
the environment to solve such problems. GAMS, originally developed by the
World Bank for large scale economic modelling, is a flexible system that is
ideal for developing large scale problems. Several solvers, (LP, MILP, NLP
(non-linear programme) and MINLP (mixed integer non-linear programme))
are available in GAMS.


       Although GAMS has a suite of state-of-the-art solvers for a variety of
optimisation problems, it does not specifically deal with such models. The
size of our problem and its complexity make it difficult to solve and we will
need to develop a specialised solution strategy. The strategies will consist of
exact linearizations, model reduction, model pre-processing, and variable
elimination. The design of heuristics to provide a bound on the optimal
solution will also be investigated. we will use the previous work on these
types of problems (Ponnambalam et al., 1992; Kudva et al., 1994; and
Elkamel et al., 1998) as a base on which to build a solution strategy to the
current model.


5) Relevance to department,university and Kingdom

    Saudi Arabia is the largest oil producing country in the world. Oil
processing plants, refineries, and petrochemical plants scattered allover the
country and omit huge amount of air pollutants to the atmosphere.
Controlling air pollutants is an important issue for Saudi Arabia. The
proposed work model can be applied later on to many of the oil and
petrochemical plants in Saudi Arabia. Chemical Engineering Department at
KFUPM needs a faculty who works in the proposed area of research.
Currently nobody in the department specialized in this area which is of great
importance to the department and country. Dr. Al-Ali is specialized in
transport phenomena and most of his publication in this area. In addition to
transport phenomena courses (undergraduate and graduate) Dr. Al-Ali


                                      8
teaches Separation Operations and Petroleum Refining courses in the
department. Air pollution is mainly described by diffusion mass transfer
which is an important part of transport phenomena. Dr. Al-Ali will teach an
undergraduate course entitled Process Air Pollution Control (CHE 470) next
semester (term 062). From the above mentioned facts Dr. al-Ali is a suitable
candidate to fill full the vacant position in the department. In addition to the
above mentioned contacts with scientists working in this important area will
much appreciated.

6) Publication Plan

      I expect to publish two papers in a refereeed journals and one
conference presentation from this research. Possible journals for publicaton
are:

1. Journal of Environment and Pollution
2. Journal of Environmental Engineering Science

7) Suitability of Host Institution

      In Waterloo University I will work with Dr. Ali Elkamel and his
group. Dr. Elkamel is actively working in air pollution control and
optimisation. Appendix C shows some of the recent publication of Dr.
Elkamel.

8) Budget

      total amount of 37000 is needed for computer, software, publication
fee and publication expenses. Breakdown of the cost is as follows:

Attending conference                    15000 SR
Computer                                  7000 SR
Publication cost                          8000 SR
Dispersion model software                 7000 SR
                                        _________
Total                                    37000 SR




                                       9
9) Conclusion

      In this sabbatical leave proposal, a new model for air pollution control
from refineries will be considered. Pollution control from refineries is an
important issue in Saudi Arabia. Working for one year in this important field
and interaction with scientists from a very reputed institute such as Waterloo
University will add to the auther scientific Ability. The proposed model will
be applied to Saudi refineries afterwards.




                                     10
10) REFERENCES

-   A. Elkamel, A. Elgibaly and W. Bouhamra, “Optimal Air Pollution Control
    Strategies : The Retrofitting Problem”, Advances in Environmental Research, 2 (3),
    375-389, (1998).

-   Flagan, R.G. and J.H. Seinfeld, 1988. Optimal Air Pollution Control Strategies. In
    Fundamentals of Air Pollution Engineering. Prentice Hall, Englewood Cliffs, New
    Jersey 07632.

-   Guldmann, J.-M. and Shefer, D., 1980. Air Pollution Impacts and Control. In
    “Industrial Location and Air Quality Control – A Planning Approach”, John Wley &
    Sons, USA, pp.63-101.

-   Haq, I., Kumar, S., and Chakrabarti, SP., 1997. Cost-Benefit Analysis of Control
    Measures in Cement Industry in India. Environment International, Vol.23, No.1,
    pp.33-45.

-   Holnicki, P., 1994. Air Quality and Emulsion Abatement in Regional Scale. In
    Computer Simulation, Air Pollution II Vol. 1., Edited by Baldasano, J.M., et al.,
    Computational Mechanics Publications, Southampton Boston.

-   Jackson, Jr., W.E. and Wohlers, H., 1970. Determination of Regional Pollution
    Control Costs and the Cost of Air Pollution Reduction in the Delware Valley.
    Environmental Engineering and Science, Drexel University, Philadelphia, Pa, 19104.

-   Kemner, W.F., 1979. Cost Effectiveness Model for Pollution Control at Coking
    Facilities. Research Report US EPA-600/2-79-185, August.

-   Kohn, R.E., 1968. A Mathematical Programming Model for Air Pollution Control.
    Paper presented at the 1968 Annual Convention of the Control Association of Science
    and Mathematics Teachers, November 30, 1968, St. Louis, Missouri.

-   G. Kudva; A. Elkamel, J.F. Peknyc, and G.V. Reklaitis, “Heuristic Algorithm for
    Scheduling Batch and Semi-Continuous Plants with Production Deadlines,
    Intermediate Storage Limitations, and Equipment Changeover Costs", Computers and
    Chemical Engineering, Vol. 18, No. 9, 859-875 (1994).

-   Lou, J.C., Chung and Chen, K.S., 1995. An Optimization Study of Total Emission
    Controls fot Particulte Pollutants. J. Environmental Management, Vol.43, pp.17-28.

-   Pirrone, N. and Batterman, S.A., 1995. Cost-Effective Strategies to Control Radon in
    Residences. J. Environmental Engineering, Vol. 121, No. 2, February, pp.120-131.




                                            11
-   Ponnambalam, K., A. Vannelli and S. Woo, 1992. An interior point implementation
    for solving large planning problems in the oil refinery industry, Can. J.of Chem.
    Engrg, 70(2), 368-374.

-   Rossiter, A.P., Kumana, J.D., and Ozima, M.K., 1994. Ranking Options for Pollution
    Prevention and Pollution Control Using Graphical Methods. Adapted from "Rank
    Pollution Prevention and Control Options,” Chemical Engineering Progress, February
    1994.

-   Schweizer, P.F., 1974. Determining Optimal Fuel Mix for Environmental Dispatch.
    IEEE Trans. Automatic Control, October, pp.534-537.

-   Shaban, H.I., Elkamel, A., and Gharbi, R., 1997. An Optimization Model for Air
    Pollution Control Decision Making. Environmental Modelling & Software, Vol.12,
    No.1, pp.51-58.




                                          12
11. CURRICULUM VITAE

1. Name & Date of Birth          :Habib Hussain Al-Ali,01-04-1954

2. Nationality                   :   Saudi

3. Marital Status                :   Married

4. Academic Rank                 :   Associate Professor

5. Present Address               :   P.O. Box 1535, KFUPM, Dhahran, Saudi Arabia
                                     email    hhali@kfupm.edu.sa
                                     Tel.    Office (03) 860-2599
                                            Home (03) 860-5578
                                     Mobile    05059-24232
6. Degrees:

   1988        Doctor of Philosophy (Chemical and Petroleum Refining Engineering),
   from the    Colorado School of Mines, Colorado, U.S.A.

   1982      Master of Science (Chemical Engineering) from the University of
   Petroleum &     Minerals, Dhahran, Saudi Arabia.

   1979        Diploma in the German Language, Goethe Institute, West Germany.

   1978        B.Sc. of Chemical Engineering Science from the University of Petroleum
   &           Minerals, Dhahran, Saudi Arabia.

7. Years of Service:

   1997   -   present Associate Professor
   1988   -   1997    Assistant Professor
   1982   -   1983 Lecturer
   1980   -   1982 Graduate Assistant

8. Other Related Experience:

   June -Sept. 1977    Worked with ARAMCO as an Engineer Trainee in the Oil
   Processing Department.


                                             13
   June -Sept. 1978     Worked with ARAMCO as an Engineer Trainee in the
   OilProcessing Department.

   Summer 1991 Acting Chairman,Chemical Engineering Department, KFUPM.

   Summer 1993 Acting Chairman,Chemical Engineering Department, KFUPM.

   Summer 1995 Participated in the British Summer Research Program, Coventry
   University, England.
   Summer 1998 Researcher, Ras Tanura Refinery, Saudi ARAMCO.


9. Publications :

   Papers in Refereed Journals:

   1)   Said, A.S. and Al-Ali, H. H., "The Non-Linear Isotherm From Extraction to
        Chromatography", Journal of HRC & CC, 1981, Vol. 4, pp. 474-476.

   2)   Said, A.S., Al-Ali, H., and Hamad, E., "Mathematics for Chromatography - Part
        I: Concentration Distribution and Their Moments", Journal of HRC & CC,
        1982, Vol. 5, pp. 306-310.

   3)   Said, A.S., Al-Nafea, M., Al-Ali, H., and Hamad, E., "Concentration Profiles
        and Breakthrough Curves in Non-Linear Chromatography", Journal of HRC &
        CC, 1982, Vol. 5, pp. 674-680.

   4)   Al-Ali, Habib H. and Selim, Sami M., "Simultaneous Development of Velocity
        and Temperature Profiles in the Entrance Region of a Parallel-Plate Channel:
        Laminar Flow with Uniform Heat Flux", Chemical Engineering
        Communication, 1992, Vol. 112, pp. 1-19.

   5)   Al-Ali, Habib H. and Selim, Sami M., "Analysis of Laminar Flow Forced
        Convection Heat Transfer with Uniform Heating in the Entrance Region of a
        Circular Tube", Canadian J. Chem. Eng., 1992, Vol. 70, pp. 1101-1107.

   6)   Al-Ali, Habib H. and Selim, Sami M., "Momentum and Heat Transfer in the
        Entrance Region of a Parallel - Plate Channel: Developing Laminar Flow with
        Constant Wall Temperature", Applied Scientific Research, 1993,  Vol. 51, pp.
        663-675.

   7)   Demirel, Y. and Al-Ali, H. H., "Thermodynamic Analysis of Connective Heat
        Transfer in a Packed Duct with Asymmetrical Wall Temperatures", Int. J. Heat
        Mass Transfer, Vol. 40, No. 5, pp. 1145-1153, 1997.

   8)   Chebbi, R., Al-Ali, Habib H., and Selim, Sami M., “Simultaneously
        Developing Laminar Flow and Heat Transfer in the Entrance Region of a


                                        14
     Circular Tube with Constant Wall Temperature”, Chemical Engineering
     Communication, Vol. 160, pp. 59-70, 1997.

9)   Demirel, Y., Al-Ali, H.H., and Abu-Al-Saud, BA, “Entropy Generation of
     Convection Heat Transfer in an Asymmetrically Heated Packed Duct”, Int.
     Communications in Heat and Mass Transfer, Vol. 24, No. 3, pp. 381-390, 1997.

10) Makkawi, Y., Demirel, Y. and Al-Ali, H. H., " Numerical Analysis of
    Convective Heat Transfer in a Rectangular Packed Duct with Asymmetric
    Heating", Energy Conversion Management, Vol. 39, pp. 455-463, 1998. .

11) Demirel, Y., Abu Al-Saud, B. A., Al-Ali, H. H., and Makkawi, Y., "Packing
    Size and Shape Effects on Forced Convection in Large Rectangular Ducts with
    Asymmetric Heating”, Intl. J. Heat Mass Transfer, Vol. 42, pp. 3267-3277,
    1999.

12) H. H. Al-Ali, M. S. Al-Gallaf, Y. Demirel, "Numerical Analysis of Transient
    Heat Transfer in a Rectangular Packed Duct with Asymmetric Heating”,
    Submitted to Intl. J. Heat Mass Transfer, January 1999.

13) Demirel, Y., Al-Ali, H.H., and Abu-Al-Saud, B.A., “Enhancement of
    Convection Heat Transfer in a Rectangular Duct”, Applied Energy, Vol. 64, pp.
    441-451, 1999.

14) Y. Demirel, R. N. Sharma, H. H. Al-Ali, " On the Effective heat Transfer
    Parameters in a Packed Bed”, Intl. J. Heat Mass Transfer, Vol. 43, pp. 327-332,
    2000.

15) A.M. Aitani, M.F.Ali, and H.H. Al-Ali “A Review of Non-Conventional
    Methods for the Desulfurization of Residual Fuel Oil” Petroleum Science and
    Technology, 18(5&6), 537-553 (2000)
18) A.A.Kareeri, H.D Zughbi, and H.H. AlAli, „Simulation of Flow Distribution in
   Radial Flow Reactor” Ind. & Engineering Chem. Research (IECR),45,2862-
   2874,2006.

Papers in Refereed Conferences:

1)   Bell, I. A., Al-Ali, Habib, and Al-Daini, A. J., “Experimental Parametric
     Studies of the Thermal Performance of Solar Collectors”, World Renewable
     Energy Congress IV, 15-21 June 1996, Denver, Colorado, USA.

2)   Bell, I. A., Al-Daini, A. J., Al-Ali, Habib, Abdel-Gayed, R. G., and Duckers,
     L., “The Design of an Evaporator/Absorber and Thermodynamic Analysis of a
     Vapor Absorption Chiller Driven by Solar Energy”, World Renewable Energy
     Congress IV, 15-21 June 1996, Denver, Colorado, USA.




                                      15
   3) Demirel, Y., Al-Ali, H.H., and Abu-Al-Saud, B.A., “Volumetric Entropy
      Generation of Connective Heat Transfer in an Asymmetrically Heated Packed
      Duct”, 4th International Symposium on Heat Transfer in Beijing, pp. 520-525,
      October 7-11, 1996.

   4)   Demirel, Y., Al-Ali, H.H., and Abu-Al-Saud, B.A., “Convection Heat Transfer
        in a Large Rectangular Packed Duct with Asymmetrical Heating”, 7th
        AIAA/ASME Joint Thermophysics and Heat Transfer Conference Albuquerque,
        New Mexico, USA, June 15-18, 1998.

   5)   Demirel, Y., Al-Ali, H.H., and Abu-Al-Saud, B.A., “Enhancement of
        Convection Heat Transfer in a Rectangular Duct”, The 7th International Energy
        Conference and Exhibition ENERGEX ’98, Manama, Bahrain, November 19-
        21, 1998.

   6)   A. Kareeri,H. Zughbi and H.H. AlAli, „Simulation of ContinuousCatalyst
        Regeneration Reactor‟ presented in Australian Chemical Engineering
        Conference (Chemica) Sep. 26-29,2004.

   7)   A. Kareeri,H. Zughbi and H.H. AlAli, „Simulation of Flow in a Flow Fixed Bed
        Reactor (RFBR)‟ presented in Norway Fourth International Conference on
        Computational Fluid Mechanics June 6-8 2005.

10. Master and PhD Thesis :

   Advising :
  Y. T. Makkawi, June 1995, Investigation of Heat Transfer in a Rectangular Packed
  Ductwith Constant Heat Flux and Asymmetrical Wall Temperature.
   M.S. Al-Gallaf, January 1999, Investigation of Transient Heat Transfer in a
   Rectangular Packed Duct with Asymmetric Heating.
   A. Kariri, December 2004, Simulation of Flow Profiles in the Continuous Catalyst
   Regeneration (CCR) Platformer Reactors.
   Co-Advising::
   B. Abu Al-Saud, November 1997, Heat Transfer in Rectangular Packed Duct.
   Committee Member :
   N. A. Al-Baghli, June 1994, Adsorption of Light Gases and Their Mixtures on SR-
   115 and ETS-10 Zeolites.
  M. M. Hossain, April 2000, Experimental Investigation of
  Hydrogen Spillover on Co-Clay Hydrotreating Catalysts
  Noble Metals.




                                        16
 R. Khan, March 2003, Hydrogen Spillover Effects in Noble
 Metal Promoted Catalysts.

 I. Khan, March 2003,Deactivation of Dehydration
 Adsorbents.

 R. Shaikh, June 2004, Investigation of Flow in
 Partiallly Packed Vessels.

N. Saifi, May 2004, Numerical Analysis of Entropy Generation in Laminar Viscous
Fluid Flow in Rectangular Ducts.

S. Siddigui, June 2004, Numerical and Experimental Studies of non Reactive Mixing.

A Al-Salman, December 2004, Simulation of Melting Ice in 2D and 3D.




                                        17
12. Appendicies

   a. Appendix A

Table 1: Imperial Oil Refineries that Contribute to Air Pollution

(according to the pollution watech website http://www.pollutionwatch.org of the
Canadian Environmental Law Association.



Sarnia Refinery Plant
602 South Christina Street
SARNIA, ON, N7T 7M5

Total                                            27,374,646
Sulphur dioxide                                  20,704,346
Oxides of nitrogen                                2,628,461
Carbon monoxide                                   2,077,732
PM - Total Particulate Matter                       996,165
Volatile Organic Compounds (VOCs)                   967,942
PM10 - Particulate Matter <=is 10 Microns           778,090
PM2.5 - Particulate Matter <= 2.5 Microns           443,793



Dartmouth Refinery
600 Pleasant St
Dartmouth, NS, B2Y 3Z7

Total                                            14,911,181
Sulphur dioxide                                   5,967,941
Carbon monoxide                                   3,811,232
Oxides of nitrogen                                3,593,306



                                            18
Volatile Organic Compounds (VOCs)                 918,819
PM - Total Particulate Matter                     619,883
PM10 - Particulate Matter <=is 10 Microns         462,520
PM2.5 - Particulate Matter <= 2.5 Microns         282,179




Strathcona Refinery
Hwy 16A East & 34 St.
Edmonton, AB, T6P 1X7

Total                                            9,553,226
Sulphur dioxide                                  5,330,213
Oxides of nitrogen                               1,989,383
Volatile Organic Compounds (VOCs)                  953,304
Carbon monoxide                                    782,956
PM - Total Particulate Matter                      497,370
PM10 - Particulate Matter <=is 10 Microns          363,174
PM2.5 - Particulate Matter <= 2.5 Microns          227,075



Nanticoke Refinery
P.O. Box 500
Nanticoke, ON, N0A 1L0

Total                                            9,527,463
Sulphur dioxide                                  5,819,973
Oxides of nitrogen                               2,096,784
Volatile Organic Compounds (VOCs)                  725,333
Carbon monoxide                                    572,615
PM - Total Particulate Matter                      312,758
PM10 - Particulate Matter <=is 10 Microns          244,650
PM2.5 - Particulate Matter <= 2.5 Microns          157,077




                                            19
b. Appendix B

Letter of Invitation




                       20
b. Appendix C

Recent publication of Dr. Ali Elkamel

                                    21
J1.   G. Al-Sharrahc*, H. Geoff, and A. Elkamel, “Decision Making with
      Optimization Using Simple Multi-Objective and Strategic Tools”, Trans. I.
      Chem. E, Part A, Chemical Engineering Research and Design, 84(A10):1-
      12, October (2006).

J2.   S. Abdul-Wahabc, A. Elkamel, M. AL-Otaibi, “Building an Inferential
      Estimator for Modeling Product Quality in a Crude Oil Desalting and
      Dehydration Process”, Chemical Engineering and Processing, 45 (7): 568-577,
      JUL (2006).

J3.   F. Chui*, A. Elkamelc, and M. Fowler, “An Integrated Decision Support
      Framework for the Assessment and Analysis of Hydrogen Production
      Pathways”, Energy & Fuel, 20 (1): 346-352 JAN-FEB (2006).

J4.   G. Zahedi*, A. Jahanmiri, A. Elkamelc, and A. Lohi, “Mathematical Modeling
      of CO2 Removal in a Turbulent Contact”, Chem. Eng. Technol., 29, No. 8, 916-
      922, (2006).

J5.   M. Ba-Shammakh*, A. Elkamelc, P. Douglas, and E. Croiset, “A Mixed-Integer
      Nonlinear Programming Model for CO2 Emission Reduction in the Power
      Generation Sector”, International Journal of Environment and Pollution, 37
      pages, in press (2006).

J6.   G. Zahedi*c, A. Elkamel, and A. Lohi, “Enhancing CO2 Conversion Using
      Dynamic Optimization Applied on Shell Temperature and Inlet Hydrogen
      During 4 years Operation of Methanol Plant”, Energy Sources, 17 pages, in
      press (2006).

J7.   F. Chui*, A. Elkamelc, and E. Fatehifar*, “Developing New Correlations and
      Uncertainty Analysis for Ground and Maximum Ground Level Concentrations
      Predictions”, International Journal of Environment and Pollution, 26 pages, in
      press (2006).

J8.   E. Fatehifar*, A. Elkamelc, M. Taheri, W. A. Anderson, and S. A. Abdul-
      Wahab, “Modeling and Simulation of Multi-Pollutants Dispersion from a
      Network of Refinery Stacks Using a Multiple Cell Approach”, Journal of
      Environmental Engineering Science, 27 pages, in press (2006).

J9.   E. Fatehifar*, A. Elkamelc, and M. Taheri, “ A MATLAB-Based Modeling and
      Simulation Program for Dispersion of Multi-Pollutants from an Industrial
      Stack for the Educational Use in a Course in Air Pollution Control”, Computer
      Applications in Engineering Education, 24 pages, in press (2006).

J10. F. Bellamine and A. Elkamelc, "Numerical Characterization of Distributed
     Dynamic Systems Using Tools of Intelligent Computing and Generalized


                                        22
     Dimensional Analysis", Applied Mathematics and Computation, 35 pages, in
     press (2006).

J11. S. Al-Gharib*, A. Elkamelc, and C. Baker, "A Multi-Criteria Decision Approach
     for Choosing and Ranking SO2 Emission Reduction Measures for a Network of
     Power Stations", World Review of Science, Technology and Sustainable
     Development, 47 pages, in press (2006).




                                       23

								
To top