Information for

Ali Elkamel



BSc, Chemical Engineering, Colorado School of MinesPicture of Ali Elkamel

BSc, Mathematics, Colorado School of Mine

MSc, University of Colorado-Boulder

PhD, Purdue University

Research interests

  • Energy and Environmental Engineering Systems
  • Air Pollution Modeling, Simulation, and  Control
  • Refinery modeling, planning, and optimization
  • Sustainable Development of the Petrochemical Industry
  • Planning and Scheduling of Process Operations
  • Dynamic Modeling and Optimization
  • Combinatorial Optimization
  • Soft Computing
  • Uncertainty in Optimization
  • Process Modeling and Simulation
  • Computer-Aided Product Formulation and Design

Traditionally, PSE has been concerned with the understanding and development of systematic procedures for the design and operation of chemical and biochemical process systems, ranging from microsystems to industrial scale. Recently, the scope of PSE has been broadened to include systems at larger scales such as supply chains and the business enterprise, and at much smaller scales such as molecular and atomic systems. The use of PSE enables companies to operate inherently safe processes while at the same time reduce production costs, improve quality, increase efficiency, reduce pollution, and bring products to market faster. The long term research objective of my group is to further extend upon the existing PSE toolset and integrate environmental impact assessment tools. Unlike the traditional approach that focuses on man-made systems and treats environmental requirements as constraints, we will stress on the interactions between process systems and the natural system with the aim of establishing a harmonious social-economical-technological combined systems.

Our focus will be on improvements of the existing methodologies in order to create and operate processes which will have minimum environmental impacts and maximum economic benefits in order to reach sustainable development. The consideration of economics along with environmental issues is essential in order to preserve profitability; otherwise investment will not occur and environmental protection will be eroded. Models and tools that can be used to improve the efficiency and sustainability of products and processes will be investigated. These methods will stress on the interactions between the industrial and ecological systems and will treat them as networks of interconnected flows. Our ultimate goal is to contribute to the development of advanced process systems analysis tools that are able to deal with sustainability in a systematic way and will be able to produce ecologically and economically conscious process systems. We envision a set of robust tools that can be used at a variety of decision points in product and process design; including: (1) Process modifications on existing processes to reduce waste and energy consumption, improve efficiency, and use renewable resources (2), Scheduling process operations to minimize pollution under adverse environmental conditions or under changing regulations, (3) Selection of appropriate pollution reduction options from a superstructure of available technologies, (4) Development of new sustainable processes that abide to economic, safety, and environmental considerations, and (5) Suggest robust green energy and power production pathways. At all decision stages,  the focus will be on the use of less energy, lower amounts of non-renewable resources, less waste generation, and an increase in the use of renewable resources while at the same time taking into consideration the contribution of ecosystems and hence adopting a broader view which requires an expansion of the analysis boundaries.

Process scheduling

During the last few years, batch and semi-continuous chemical processing has received increasing attention as a mode of production for high value-added specialty chemicals. A typical facility processes a number of related products on a given set of equipment. The sharing of equipment necessitates the scheduling of production tasks.

Our goal in the area of process scheduling is to develop effective mathematical programming formulations and solution strategies that exploit the structure of the scheduling problem and processing facilities. In addition, we are concerned with scheduling of start-ups, shut downs, and maintenance of facilities.

In a real processing plant, there is always an uncertainty factor associated with various parameters such as market demand, processing time, and resource availability. We are taking two complementary approaches for handling uncertainties in scheduling process operations. First, the scheduling models must be formulated to be robust (stable) under variations. Stabilized formulations that are able to absorb disturbances are being considered and performance robustness criteria are being defined. The second approach we are taking for handling uncertainties is the development of a reactive scheduling strategy that is able to recommend new schedules while keeping the original scheduling decisions intact as much as possible.

Pollution prevention and waste minimization

Pollution prevention is one of the most serious challenges that are currently facing the industry. With increasingly stringent environmental regulations, there is a growing need for cost and energy efficient pollution prevention techniques. Our research in the area of pollution prevention has been focusing on the integration of the systems methodology to pollution problems.

In general, pollution prevention can be divided into two categories: short-term prevention and long-term prevention. Our aim in the area of short-term pollution prevention is to develop solutions at the operational level by incorporating our background and expertise in process scheduling. It is a well-known fact that the implementation of operational modifications often requires the least capital when compared to other prevention strategies. To this end, we are now looking at reactive scheduling to absorb disturbances that cause violations of environmental standards. We are also preparing scheduling models with the objective of determining schedules that minimize waste and abide to environmental constraints.

Long-term pollution prevention involves the determination of strategies that must be implemented to meet environmental standards over a long period. There are usually an infinite number of possibilities that must be screened economically. We have successfully prepared and solved mathematical programming models for the selection and planning of air pollution prevention options. Both the primal selection problem and the retrofit problem were considered. These models offer the appropriate scheme for evaluating and ranking available waste minimization and pollution prevention technologies. Later, we plan to look at means of measuring wastes in a process. These means will be integrated with the pollution selection models and equipment cost equations to arrive at comprehensive models that can be used as an engineering tool for simultaneously evaluating, planning, and designing pollution prevention strategies on a continuous basis.

Process modification is another long-term strategy for pollution prevention. These modifications involve the way a production process is operated and maintained. Process conditions as well as reaction parameters can play an important role in waste minimization. We are currently developing mathematical programming models to suggest process modifications for a number of industrial processes.

A longer-term strategy for pollution prevention is to develop sustainable processes during the planning and design stage. The quest for pollution prevention and increased pressure and demand for environmentally sustainable processes and products have been creating new rules in the process industry. Sustainability is defined as “economic development that meets the needs of the present generation without compromising the ability of future generations to meet their own needs”. We have recently prepared a mixed integer linear programming model and solution strategy for the development of a petrochemical industry with sustainability as the objective. The model can account for the different interactions among units and provides at the same time a suitable mathematical representation of the decision variables of interest. We plan to extend the above models so that safety is also incorporated in the planning stage as well as product selection and design.

CO2 capture, storage and mitigation

In this group of projects we seek new solutions to one of the grand challenges of this century: supplying energy to a growing population while reducing greenhouse gas emissions. Since no single technology is likely to be able to meet this ultimate energy challenge of the future on its own, it is essential to use a systems approach that can provide insight and data on how viable a technology can be. Our long term vision is to propose optimal solutions to effectively manage carbon dioxide reduction, capture, and sequestration while meeting growing energy demands. These carbon management solutions shall include physical and natural processes associated with decarbonization; carbon dioxide capture, transport, and sequestration; the use of new and/or improved fuel sources (nuclear, fossil fuels, hydrogen energy, renewables, etc.); improved efficiency of energy conversion and utilization; economic and market analysis; and alternative energy policy options.

The novelty in this research is that it represents a multi-region, multi-technology decision framework that will provide provincial and national strategies for the effective reduction of carbon dioxide. It is based upon a bottom-up view of industrial activities and a top-down view of energy and other product demands. The framework will also account for the predictable trends and interactions that occur in a dynamic setting of a metropolitan region or a country as a whole. Included within this last category will be factors of regional growth, technological development, availability and limitations on resources, and interrelationships between different industrial sectors. One major contribution from this research will be the development of a decision support system that can aid management and policy makers in constructing equitable comparisons among different carbon dioxide abatement proposals. This will permit the selection of the least cost solutions from among a series of alternate carbon dioxide reduction schemes.

Mixed-integer programming and uncertainty in optimization

Integer and mixed integer programs (MIP) can be used to model a wide variety of problems encountered in many areas including plant layout and design, process scheduling, pollution prevention, and even molecular simulation and design. The general MIP is, however, known to belong to the class NP-complete or hard combinatorial optimization problems and no technically good algorithm is known to be available for its solution. Our aim here is to develop novel modeling techniques and effective solution methods for optimization problems.

The presence of various uncertainties (uncertainty in price, production rates and costs, labor, demand, raw material availability, etc.) complicates the optimization process. Traditionally, the treatment of uncertainty is realized by the use of a stochastic optimization approach. This approach recognizes the presence of multiple data instances that might be potentially realized in the future. The optimization will then attempt to generate a decision that maximizes (or minimizes) an expected performance measure, where the expectation is taken over the assumed probability distribution. In many cases, when multiple uncertain factors exist in the input data, assumptions of distributional independence among factors are made. After possible data instances (scenarios) or probability distributions are fed into a model, a stochastically optimal solution is generated.

There are multiple drawbacks of the stochastic approach in handling uncertainty. First of all, a decision has to be made on probabilities to the various data instances (future scenarios) or probability distributions for the different uncertain factors. Assigning such probabilities is far from a trivial exercise for many decision makers. Another more important drawback is that every decision has associated with it a whole distribution of outcomes, depending on what data scenario is actually realized. Decision makers are more interested in having information about the whole distribution of outcomes and the risk of poor system behavior. Clearly, a more robust approach is needed. We are looking at two different novel approaches for handling uncertainties: interval mathematics and fuzzy set theory.

Selected references

  • A systematic computer-aided product design and development procedure: case of disinfectant formulations”, Industrial and Engineering Chemistry research, in press, (2012).
  • “A modeling study of the effect of carbon dioxide mitigation strategies, natural gas prices and steam consumption on the Canadian Oil Sands operations”, Energy, 45(1), pages 1018-1033, (2012).
  •  “Analysis of Ontario’s hydrogen economy demands from hydrogen fuel cell vehicles,” International Journal of Hydrogen Energy, vol. 37(11), pp. 8905–8916, 2012.
  • “Integration of Nuclear Energy and Water Management into the Oil Sands Operations”, AIChE J., in press, (2012).
  •  “Optimized production of hydrogen in an eco-park network accounting for life-cycle emissions and profit”, International Journal of  Hydrogen Energy, 37(6), pages 5347-5359, (2012).
  • “Optimization of petroleum refinery water network systems retrofit incorporating reuse, regeneration and recycle strategies”, Canadian Journal of Chemical Engineering, 90(1), pages 137-143, (2012).
  • “Effect of cholineacetyltransferase activity and choline recycle ratio on diffusion-reaction modeling, bifurcation and chaotic behavior of acetylcholine neurocycle and their relation to Alzheimer's and Parkinson's diseases”, Chemical Engineering Science, 68 (1), pages 19-35, (2012).
  • “Application of continuation method and bifurcation for the acetylcholine neurocycle considering partial dissociation of acetic acid,” Computers and Chemical Engineering, 46, pages 78-93, (2012).
  • “Integrated energy optimization model for oil sands operations”, Industrial and Engineering Chemistry Research 50 (22), pp. 12641-12663, (2011).
  • “Predictive model of pervaporation performance based on physicochemical properties of permeant-membrane material and process conditions”, Journal of Membrane Science 381 (1-2), pages 1-9, (2011).
  • "Computer Facilitated Generalized Coordinate Transformations of Partial Differential Equations with Engineering Applications", Computer Applications in Engineering Education, 19 (2), pages 365-376, (2011).
  • “Sustainable convergence of electricity and transport sectors in the context of a hydrogen economy”, International Journal of Hydrogen Energy, 36 (11), pages 6357-6375, (2011).
  • “Dimensional analysis and scale-up of immiscible two-phase flow displacement in fractured porous media under controlled gravity drainage”, Energy and Fuels, 25 (4), pages 1731-1750, (2011).
  • “Prediction of isoflavone extraction from soybean meal using supercritical carbon dioxide with cosolvents”, Chemical Engineering Journal, v 172, n 2-3, pages 1023-1032, (2011).
  • “Optimization and sensitivity analysis of an extended distributed dynamic model of supercritical carbon dioxide extraction of nimbin from neem seeds" Journal of Food Process Engineering, Vol. 34, Issue 6, pages 2156-2176, (2011).
  • “A Robust Optimization Approach for Planning the Tranition to Plug-in Hybrid Electric Vehicles”, IEEE Transactions on Power Systems, 26 (4), art. no. 5720537, pages 2264-2274 , (2011).
  • “A Robust distributed model predictive control algorithm”, Journal of Process Control, 21 (8), pages 1127-1137, (2011).
  • “Control Vector Optimization and Genetic Algorithms for Mixed-Integer Dynamic Optimization in the Synthesis of Rice Drying Processes”, invited paper, Journal of the Franklin Institute  (special issue on Modeling, Simulation, and Applied Optimization), 348 (7), pages 1318-1338, (2011).
  • “Combining Design of Experiments Techniques, Connectionist Models, and Optimization for the Efficient Design of New Product Formulations”, Chemical Product and Process Modeling, Volume 5, Issue 1, Article 11, pages 1-18, (2010).
  • “Dynamic Modeling and Optimization of a Batch Reactor for Limonene Epoxidation”, Industrial & Engineering Chemistry Research, 49 (18), pages 8369 – 8378, (2010).
  • “Strategic Planning of Integrated Multirefinery Networks: A Robust Optimization Approach Based on the Degree of Conservatism”, Industrial & Engineering Chemistry Research, 49, pages 9970 – 9977, (2010).
  • “Robust Planning of Multisite Refinery Networks: Optimization under Uncertainty”  Computers and Chemical Engineering, 34, pages 985 – 995, (2010).
  • “A Multi-Period Optimization Model for Energy Planning with CO2 Emission Consideration”, Journal of Environmental Management, 91, pages 1063-1070, (2010).
  • “Designing and Testing a Chemical Demulsifier Dosage Controller in a Crude Oil Desalting Plant: An Artificial Intelligence Based Network Approach”, Chemical Engineering Technology, 33, No. 6, pages 973-982, (2010).
  • “Generalized Disjunctive Programming for Synthesis of Rice Drying Processes”, Industrial & Engineering Chemistry Research, 49, pages 2312-2325, (2010).
  • “Petroleum Refinery Operational Planning  using Robust Optimization”, Engineering Optimization, Vol. 42, No. 12, pages 1119 – 1131, (2010).
  • “A Systematic Statistical based Approach for Product Design: Application to Disinfectant Formulations”, Industrial & Engineering Chemistry Research, 49, pages 204-209, (2010).
  • “A Bayesian Experimental Design Approach for Assessing New Product Performance: An Application to Disinfectant Formulation.”, Canadian Journal of Chemical Engineering, Volume 88, Issue 1, pages 88-94,  (2010).
  • “Selection of Control Structure for Distributed Model Predictive Control in the Presence of Model Errors”, Journal of Process Control, 20, pages 270-284, (2010). Appears on the list of  "Top 25  Hottest Articles on
  • “Optimal Transition to Plug-in Hybrid Vehicles in Ontario-Canada Considering the Electricity Grid Limitations,” IEEE Transactions on Industrial Electronics, special issue on “Plug-in Hybrid Electric Vehicles”, Vol. 57, No. 2,  pp. 690-701, (2010).  Recognized among the most cited papers of the IEEE Transactions on Industrial Electronics (ranked 15 out of 473 papers published in 2010).
  • “Sustainability indicators for decision-making and optimisation in the process industry: The case of the petrochemical industry”, Chemical Engineering Science, Volume 65, Issue 4, pages 1452-1461, (2010).
  • “Air quality and environmental impacts of alternative vehicle technologies in Ontario, Canada”, International Journal  of  Hydrogen Energy, 35, pages 5145 - 5153, (2010).
  • "Optimization of Energy usage for Fleet-Wide Power Generating System Under Carbon Mitigation Options", AIChE J, Vol. 5, No. 2, pages 3168-3190, (2009).
  • "Long term electricity demand forecasting for power system planning using economic, demographic, and climatic variables", European  Journal of Industrial Engineering, Vol. 3, No. 3, pages 277-304, (2009).
  • “Effect of Choline and Acetate Substrates on Bifurcation and Chaotic Behavior of Acetylcholine Neurocycle and Alzheimer's and Parkinson's Diseases”, Chemical Engineering Science, 64, pages 2096-2112, (2009).
  • “Optimal Design of Hybrid Air Stripping Pervaporation System for the Removal of Multi VOC from Water Streams”, International Journal of Process Systems Engineering, Vol. 1, No. 1, pages 46-65, (2009).
  • "An Environmentally Conscious Robust Optimization Approach for Planning Power Generating Systems",  International Journal of Global Warming, Vol. 1, Nos. 1/2/3, pages 307-335, (2009).
  • “Non-linear Feedback Modeling and Bifurcation of the Acetylcholine Neurocycle and its Relation to Alzheimer’s and Parkinson’s Diseases”, Chemical Engineering Science, 64, pages 69-90, (2009).
  • “Superstructure for the Optimal Synthesis of Process Flowsheets with Applications to Hybrid Membrane Systems”, Engineering Optimization, Vol. 41, No. 4, pages 327-350, (2009).
  • “Multisite Refinery and Petrochemical Network Design: Optimal Integration and Coordination”, Industrial & Engineering Chemistry Research,48, pages 814-826, (2009).
  • "An Order-Specific Algorithm for the Determination of Representative Demand Curves", Computers and Chemical Engineering, 32, pages 1373-1380, (2008).
  • "A Heuristic Optimization Approach for Air Quality Monitoring Network Design with the Simultaneous Consideration of Multiple Pollutants", Journal of Environmental Management,88, pages 507-516 (2008). Article was featured in the Environmental and Building Technologies (EBT) Alerts, Technical Insights, Frost & Sullivan ( Article is the top article in the list of top 20 articles published on the same topic since its publication, BioMedLib, September 2012.
  • "Global Optimization of Reverse Osmosis Networks for Wastewater Treatment and Minimization", Industrial & Engineering Chemistry Research, 47, pages 3060 - 3070, (2008).
  • "An Energy Optimization Model with CO2 Emissions Constraints for the Canadian Oil Sands Industry", Energy and Fuel, 22, pages 2660-2670, (2008).
  • "Defined Medium Design and Optimization for Recombinant Human Interleukin-3 Production by Streptomyces Lividans", Biotechnology and Bioengineering, Vol. 99, No. 1, pages  214 - 222, (2008). Article selected to be on the Spotlight feature of the Journal.
  • “Model Order Reduction Using neural network principal component analysis and generalized dimensional analysis", Engineering Computations, 25, 5, pages 443-463, (2008).
  • "Macro-Level Optimized Deployment of an Electrolyzer-Based Hydrogen Refueling Infrastructure with Demand Growth ", Engineering Optimization, 40, 10, pages 955-967, (2008).
  • "Multisite Facility Network Integration Design and Coordination: An Application to the Refining Industry" Computers and Chemical Engineering, 32, pages 2189-2202, (2008).
  • "Robust Optimization for Petrochemical Network Design under Uncertainty", Industrial & Engineering Chemistry Research, Vol. 47, pages 3912-3919, (2008).
  • R. KoçG, E. AlperC, E. Croiset, and A. Elkamel, ‘Partial Regeneration of Ni-based Catalysts for Hydrogen Production via Methane Cracking’, Turkish Journal of Chemistry, Vol. 32, pages 1-12, (2008).
  • "An Optimization Approach for Integrating Planning and CO2 Emission Reduction in the Petroleum Refining Industry", Industrial & Engineering Chemistry Research, Vol. 47, pages 760-776, (2008).
  • "Optimal Design of Reverse-Osmosis Networks for Wastewater Treatment" 44 pages, Chemical Engineering & Processing, 47, pages 2163-2174, (2008). Appears on the list of  "Top 25  Hottest Articles on
  • "Two-Stage Stochastic Programming with Fixed Recourse via Scenario Planning with Financial and Operational Risk Management for Petroleum Refinery Planning under Uncertainty", Chemical Engineering and Processing, Vol. 47, pages 1744-1764, (2008).
  • "Modeling the Energy Demands and Greenhouse Gas Emissions of the Canadian Oil Sands Industry", Energy and Fuels, Vol. 21, pages 2098-2111, (2007).
  • “Dynamic Optimization Strategies of a Heterogeneous Reactor for CO2 Conversion to Methanol", Energy and Fuels, Vol. 21, pages 2977-2983, (2007).
  • “Modeling and Simulation of Multi-Pollutants Dispersion from a Network of Refinery Stacks Using a Multiple Cell Approach”, Environmental Engineering Science, Volume 24, Number 6, pages 795-811, (2007).
  • "Optimal Tuning of PID Controllers for FOPTD, SOPTD and SOPTD with Lead Processes", Chemical Engineering and Processing, 47, pages 251-264, (2007). Appears on the list of  "Top 25  Hottest Articles on”.
  • “Decision Making for Petrochemical Planning Using Multi-Objective and Strategic Tools”, Trans. I. Chem. E, Part A, Chemical Engineering Research and Design, 84(A11), pages 1019-1030, November (2006).
  • “Building an Inferential Estimator for Modeling Product Quality in a Crude Oil Desalting and Dehydration Process”, Chemical Engineering and Processing, 45 (7), pages 568-577, JUL (2006).
  • “An Integrated Decision Support Framework for the Assessment and Analysis of Hydrogen Production Pathways”, Energy & Fuels, 20 (1),  pages 346-352, JAN-FEB (2006). Appears on the list of "Most-Accessed Articles: January- March 2006".
  • "Numerical Characterization of Distributed Dynamic Systems Using Tools of Intelligent Computing and Generalized Dimensional Analysis", Applied Mathematics and Computation, 182, pages 1021 - 1039, (2006).
  • “Hybrid Artificial Neural Network-First Principle Model Formulation for the Unsteady State Simulation and Analysis of a Packed Bed Reactor for CO2 Hydrogenation to Methanol”, Chemical Engineering Journal, 115, pages 113-120, (2005).
  • “A Computational Intelligence Based Approach for the Analysis and Optimization of a Crude Oil Desalting and Dehydration Process”, Energy & Fuels, 19 (6), pages 2526-2534, NOV-DEC, (2005).
  • “Modeling and Optimization of a Multistage Flash Desalination Process”, Engineering Optimization, Volume 37, Number 6, pages 591-607, (2005).
  • “Supercritical CO2 Extraction of Nimbin from Neem Seeds – A Modelling Study”,  Journal of Food Engineering, 71, pages 331-340, (2005). Appears on the list of "Top 25  Hottest Articles on".
University of Waterloo