Hassan Alfadala

Qatar University, Doha, Baladiyat ad Dawhah, Qatar

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Publications (13)8.35 Total impact

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    ABSTRACT: Most literature on the synthesis of heat exchanger networks via mathematical programming methods has dealt with phase changes by assuming nearly isothermal conditions. Many multicomponent phase changes of practical interest (e.g., those in sub-ambient processes) occur over ranges of temperatures and exhibit nonlinear temperature-enthalpy relations (T-H curve). In such cases, isothermal approximations may lead to inferior or unacceptable networks. In this article, we propose a mixed-integer nonlinear programming formulation and a solution algorithm to incorporate nonisothermal phase changes in heat exchanger network synthesis. We approximate the nonlinear T-H curves via empirical cubic correlations, and propose a procedure to ensure minimum temperature approach at all points in the exchangers. Our approach successfully solves two industry examples and shows promise for significant cost reductions when compared with existing processes. © 2009 American Institute of Chemical Engineers AIChE J, 2009
    AIChE Journal 01/2009; 56(4):930 - 945. DOI:10.1002/aic.12031 · 2.58 Impact Factor
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    ABSTRACT: Multistream heat exchangers (MSHE) enable the simultaneous exchange of heat among multiple streams, and are preferred in cryogenic processes such as air separation and LNG. Most MSHEs are complex; proprietary and involve phase changes of mixtures. Although modeling MSHE is crucial for process optimization, no such work exists to our knowledge. We present a novel approach for deriving an approximate operational (vs. design) model from historic data for an MSHE. Using a superstructure of simple 2-stream exchangers, we propose a mixed-integer nonlinear programming (MINLP) formulation to obtain a HE network that best represents the MSHE operation. We also develop an iterative algorithm to solve the large and nonconvex MINLP model in reasonable time, as existing commercial solvers fail to do so. Finally, we demonstrate the application of our work on an MSHE from an existing LNG plant, and successfully predict its performance over a variety of seasons and feed conditions. © 2008 American Institute of Chemical Engineers AIChE J, 2009
    AIChE Journal 01/2009; 55(1):150 - 171. DOI:10.1002/aic.11682 · 2.58 Impact Factor
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    ABSTRACT: Industrial cooling using seawater is a critical technology that results in one of the most profound environmental impacts on water quality and public health in Qatar and throughout the Arabian Gulf. The usage of chemical biocides to control growth of unwanted organisms leads to the discharge of enormous quantities of toxic and carcinogenic pollutants. These have a direct impact on aquatic life in the region which leads to subsequent impacts on the food chain. Additionally, there is a serious risk to human health, because the discharges are made to the same body of water used as a source of drinking water. The impact of seawater cooling becomes increasingly pronounced with the implementation of gas and chemical processing mega-projects in the industrial cities Ras Laffan and Mesaieed. The problem has been identified to be of national interest to the State of Qatar and research is underway to assess environmental impacts and identify remedial actions. This paper will report on progress in the development and application of a holistic approach to developing optimal strategies for addressing the environmental, technical, and economic issues of seawater cooling systems. Sponsored by the Qatar National Research Fund under the National Research Program scheme, the research initiative aims to provide the world with the implementation tools needed to address this problem. The project has six key aims: To develop quantitative techniques for predicting the reaction mechanisms, kinetics, and characteristics of biocides and their reaction products. To develop computational tools to predict the fate of biocides and their reaction products within the system as well as after release into the environment. To develop process optimization tools that can identify highly efficient operational strategies and retrofit design alternatives for the seawater cooling systems. To develop process integration tools that can identify highly efficient design alternatives for the industrial process that requires cooling. To conduct case studies that assess the environmental impact and optimize the operation of cooling water systems at the industrial cities of Ras Laffan and Messaieed in collaboration with the governmental and industrial project partners. To develop knowledge and problem solving capacities of decision-makers and practicing engineers in Qatar's government and industry through technology transfer and advanced training schemes. The project works towards the development of a scientific framework for sustainable strategies that address the environmental issues of seawater cooling, aid in developing sound regulatory policies, and describe short- and long-term implementation strategies.
    2008 AIChE Annual Meeting; 11/2008
  • 01/2008; Access Online via Elsevier.
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    ABSTRACT: Liquefied natural gas (LNG) is increasingly becoming an attractive alternate for crude oil and its usage is expected to grow tremendously in the coming years. Liquefaction of natural gas is a highly energy-intensive process. Because of its cryogenic nature, vapors called as boil-off gases (BOG) are generated at various places in an LNG plant due to heat leak, vapor displacement, flashing, and hot contact. The BOG from storage tanks, called as tankage BOG is usually compressed and exported to the plant fuel system. However, in some LNG plants, there exists another source of BOG; the one from intermittent loading, called as jetty BOG. As plant capacities grow and economic efficiency becomes important, it makes sense to integrate the jetty BOG optimally into the existing fuel gas network. We propose a novel superstructure and nonconvex mixed-integer nonlinear program for addressing this problem. An industrially based case study showed that our approach is efficient and practically useful.
    17th European Symposium on Computer Aided Process Engineering, Romania; 06/2007
  • Farouq S Mjalli, S Al-Asheh, H E Alfadala
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    ABSTRACT: A reliable model for any wastewater treatment plant is essential in order to provide a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. This process is complex and attains a high degree of nonlinearity due to the presence of bio-organic constituents that are difficult to model using mechanistic approaches. Predicting the plant operational parameters using conventional experimental techniques is also a time consuming step and is an obstacle in the way of efficient control of such processes. In this work, an artificial neural network (ANN) black-box modeling approach was used to acquire the knowledge base of a real wastewater plant and then used as a process model. The study signifies that the ANNs are capable of capturing the plant operation characteristics with a good degree of accuracy. A computer model is developed that incorporates the trained ANN plant model. The developed program is implemented and validated using plant-scale data obtained from a local wastewater treatment plant, namely the Doha West wastewater treatment plant (WWTP). It is used as a valuable performance assessment tool for plant operators and decision makers. The ANN model provided accurate predictions of the effluent stream, in terms of biological oxygen demand (BOD), chemical oxygen demand (COD) and total suspended solids (TSS) when using COD as an input in the crude supply stream. It can be said that the ANN predictions based on three crude supply inputs together, namely BOD, COD and TSS, resulted in better ANN predictions when using only one crude supply input. Graphical user interface representation of the ANN for the Doha West WWTP data is performed and presented.
    Journal of Environmental Management 06/2007; 83(3):329-38. DOI:10.1016/j.jenvman.2006.03.004 · 3.19 Impact Factor
  • Sameer Al-Asheh, Farouq Sabri Mjalli, Hassan E. Alfadala
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    ABSTRACT: We consider the problem of predicting the future behavior of wastewater treatment plant quality indicators by creating prediction models using historical plant data. One of the main aims of this work is to be able to predict plant operational situations in advance so that corrective actions can be taken in time. Sets of historical plant data, such as BOD, COD and TSS were collected for a local wastewater treatment plant in Doha, the capital of the State of Qatar. These variables characterize the performance of any wastewater treatment plant and can be considered as quality indicators of the plant performance. Data were collected over a period of 4 years for the influent and effluent streams of the station. The plant influent and effluent predictions were performed using different techniques. These include time-series analysis, where the ARIMA (Autoregressive Integrated Moving Average) model was implemented in this case, and two Artificial Neural Networks (ANN) algorithms, namely Adaptive Linear Neuron networks (ADALINE) and Multi-layer Feedforward (ML-FF) neural networks. The predictions from the three techniques were presented and compared. The ML-FF model predictions proved to be more reliable than that of the equivalent ARIMA predictions followed by the ADALINE predictions, particularly for the finial effluent stream variables. Copyright © 2007 The Berkeley Electronic Press. All rights reserved.
    Chemical Product and Process Modeling 01/2007; 2(3). DOI:10.2202/1934-2659.1063
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    ABSTRACT: Recent growth in world-wide consumption of natural gas highlights its immense importance as a source of primary energy. Liquefied natural gas (LNG) is the most economic way to transport natural gas over long distances. Main Cryogenic Heat Exchanger (MCHE) is a very critical equipment in an energy intensive LNG plant. To that end, modeling MCHE is the inevitable first step in the optimization of LNG plant operation. In this paper, we develop a model that is designed to simulate and predict the performance of an existing MCHE without knowing its physical details. The concept of superstructure representation is employed to derive an equivalent 2-stream heat exchanger network. The objective is to address the rating of an existing MCHE or the prediction of its performance rather than finding the area for a design or minimizing the cost. We use a mixed-integer nonlinear programming (MINLP) approach to select the best network that describes an existing MCHE. An example case is also presented to assess the ability of our model in predicting the performance of a MCHE. 2 M. M. F. Hasan et al.
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    Hassan E Alfadala, Essa Al-Musleh
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    ABSTRACT: Adopting a rigorous equilibrium stage model for simulating an amine-based Acid Gas Removal (AGR) process is not straightforward technique. It may also be a frustrating exercise in simulation. The complexity of the system is mainly attributed to the necessity of considering both chemical and phase equilibrium issues to characterize such an electrolytic system. This paper discusses an effective approach for simulating a Methyldiethanolamine (MDEA) system using Aspen Plus RadFrac equilibrium stage model. For thermodynamic modeling, the approach uses the electrolyte-NRTL model, Redlich-Kwong-Soave equation of state, and Henry's law. Component Vaporization efficiencies were incorporated in the simulation to account for the departure from equilibrium. To test its validity, the latter approach was tested against real design data obtained from a plant located in the State of Qatar. Furthermore, another process simulator namely ProMax and developed by Bryan Research and Engineering, Inc. was used for the purpose of comparison. Challenges faced during this practice (e.g., unit operation convergence, recycle convergence, etc.) and troubleshooting are also considered.
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    ABSTRACT: Exergy analysis is important and has been widely used to evaluate the thermodynamic efficiency of a variety of processes. Therefore, there is a need to develop a tool for monitoring exergy of a process in real-time and for studying the effects of various feed, equipment, process and environmental changes. The ultimate aim of this work is to develop a tool to enable dynamic and online exergy analysis in an interactive manner at various levels of equipment, process, and plant. However, in this paper, we develop methods for the online analysis of exergy in various units of a base-load liquefied natural gas (LNG) process.
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    Hassan E. Alfadala, Bilal M. Ahmad, Abdulla F. Warsame
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    ABSTRACT: The objective of this paper is to optimize the thermal performance of a fractionation unit within a liquefied natural gas (LNG) facility. Typical fractionation units in an LNG facility consisting of three distillation columns, namely de-ethanizer, de-propanizer and de-butanizer were used in this study. A hierarchical approach is developed to optimizing the system. In this approach, increasing levels of model complexity are used and various thermal targets are set and implemented. The column targeting tool available within the simulation package of Aspen Plus© software was used to optimize a fractionation unit in an LNG facility. First, integrated thermal analysis was used in identifying design targets for improvements in energy consumption and efficiency. The column targeting tool is used in the design of distillation columns by setting targets to reduce utility cost, improve energy efficiency, reduce capital investment and facilitate column debottlenecking. Starting from a short-cut distillation design calculation using the DSTWU method which is based on the well-known Fenske-Underwood-Gilliland correlations, the minimum and actual reflux ratios, minimum and actual number of stages, optimum feed location and condenser and reboiler duties were estimated. These estimates were used as starting points in the rigorous fractionation column design method RADFRAC available in Aspen Plus©. The column Grand Composite Curve (CGCC) for each column was generated to give an insight of the actual operation and guide the optimization process. Starting with appropriate feed placement, the CGCC will show the scope for reflux ratio modification by increasing the number of stages. Feed would be either preheated or precooled due to the availability of sharp enthalpy change in the condenser or reboiler side. Finally, the scope for a side condensing or side reboiling can be identified from the area beneath or above the CGCC pinch point.
    Computer Aided Chemical Engineering DOI:10.1016/S1570-7946(05)80055-0
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    ABSTRACT: The global use of natural gas is growing rapidly. This is primarily attributed to the environmental advantages it enjoys over other fossil fuels such as oil and coal. One of the key challenges in supplying natural gas is the form (phase) at which it should be delivered and according to the cost-benefit analysis for each delivery method. Natural gas may be supplied to the consumers as a compressed gas through pipelines and as hydrates delivered in special containers. Another common form is to be compressed, refrigerated and supplied as a liquid known as liquefied natural gas (LNG). When there is a considerable distance involved in transporting natural gas, LNG is becoming the preferred method of supply because of technical, economic, safety, and political reasons. Thus, LNG is expected to play a major role in meeting the global energy demands. This paper addresses the simulation and optimization of an LNG plant. First, the process flowsheet is constructed based on a typical process configuration. Then, the key units are simulated using ASPEN Plus to determine the characteristics of the various pieces of equipment and streams in the plant. Next, process integration techniques are used to optimize the process. Particular emphasis is given to energy objectives through three activities. First, the synthesis and retrofitting of a heat-exchange network are considered to reduce heating and cooling utilities. Second, the turbo-expander system is analyzed to reduce the refrigeration consumption in the process. Third, the process cogeneration is introduced to optimize the combined heat and power of the plant. These activities will be discussed in details at future publications. A case study on a typical LNG facilities is solved to examine the benefits of simulation and integration of the process. The technical, economic, and environmental impact of the process modifications are also discussed.
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    Ahmed Abdel-Wahab, Patrick Linke, Hassan Alfadala
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    ABSTRACT: Industrial cooling using seawater is a critical technology that results in one of the most profound environmental impacts on water quality and public health in Qatar and throughout the Arabian Gulf. The usage of chemical biocides to control growth of unwanted organisms leads to the discharge of enormous quantities of toxic and carcinogenic pollutants. These have a direct impact on aquatic life in the region which leads to subsequent impacts on the food chain. Additionally, there is a serious risk to human health, because the discharges are made to the same body of water used as a source of drinking water. This paper will report on progress in the development and application of a holistic approach to developing optimal strategies for addressing the environmental, technical, and economic issues of seawater cooling systems. Sponsored by the Qatar National Research Fund under the National Research Program scheme, the research initiative aims to provide the world with the implementation tools needed to address this problem.