Ali Almansoori

Readily processed protonic ceramic fuel cells with high performance at low temperatures

Cooler ceramic fuel cells Ceramic ion conductors can be used as electrolytes in fuel cells using natural gas. One drawback of such solid-oxide fuel cells that conduct oxygen ions is their high operating temperatures (at least 600°C). Duan et al. have made a proton-conducting ceramic fuel cell with a modified cathode material that exhibits high performance on methane fuel at 500°C (see the Perspective by Gorte). Science, this issue p. 1321; see also p. 1290 A proton-conduction cathode and simpler fabrication enable lower-temperature operation of methane-fueled ceramic fuel cells. [Also see Perspective by Gorte] Because of the generally lower activation energy associated with proton conduction in oxides compared to oxygen ion conduction, protonic ceramic fuel cells (PCFCs) should be able to operate at lower temperatures than solid oxide fuel cells (250° to 550°C versus ≥600°C) on hydrogen and hydrocarbon fuels if fabrication challenges and suitable cathodes can be developed. We fabricated the complete sandwich structure of PCFCs directly from raw precursor oxides with only one moderate-temperature processing step through the use of sintering agents such as copper oxide. We also developed a proton-, oxygen-ion–, and electron-hole–conducting PCFC-compatible cathode material, BaCo0.4Fe0.4Zr0.1Y0.1O3-δ (BCFZY0.1), that greatly improved oxygen reduction reaction kinetics at intermediate to low temperatures. We demonstrated high performance from five different types of PCFC button cells without degradation after 1400 hours. Power densities as high as 455 milliwatts per square centimeter at 500°C on H2 and 142 milliwatts per square centimeter on CH4 were achieved, and operation was possible even at 350°C.

Design and control of energy integrated SOFC systems for in situ hydrogen production and power generation

Abstract This paper studies the design and operation of energy integrated solid oxide fuel cell (SOFC) systems for in situ hydrogen production and power generation. Two configurations are considered: one where the hot effluent stream from the fuel cell is used directly to provide heat to the endothermic reforming reaction, and another where the hot effluent streams are mixed and combusted in a catalytic burner before the energy integration. A comparative evaluation of the two configurations is presented in terms of their design, open-loop dynamics and their operation under linear multi-loop controllers.

Multi-Objective Robust Optimization Under Interval Uncertainty Using Online Approximation and Constraint Cuts

Many engineering optimization problems are multi-objective, constrained and have uncertainty in their inputs. For such problems it is desirable to obtain solutions that are multi-objectively optimum and robust. A robust solution is one that as a result of input uncertainty has variations in its objective and constraint functions which are within an acceptable range. This paper presents a new approximation-assisted MORO (AA-MORO) technique with interval uncertainty. The technique is a significant improvement, in terms of computational effort, over previously reported MORO techniques. AA-MORO includes an upper-level problem that solves a multi-objective optimization problem whose feasible domain is iteratively restricted by constraint cuts determined by a lower-level optimization problem. AA-MORO also includes an online approximation wherein optimal solutions from the upper- and lower-level optimization problems are used to iteratively improve an approximation to the objective and constraint functions. Several examples are used to test the proposed technique. The test results show that the proposed AA-MORO reasonably approximates solutions obtained from previous MORO approaches while its computational effort, in terms of the number of function calls, is significantly reduced compared to the previous approaches.

Integration of hydrogen management in refinery planning with rigorous process models and product quality specifications

New trends of increased heavy crude markets and clean-fuel legislation, to produce ultra low-sulphur (ULS) gasoline and diesel fuels, are forcing refineries to increase their consumption of hydrogen. This critical situation raises the need to have a tool for operating refineries with flexibility and profitability. This paper addresses the planning of refinery with consideration to hydrogen availability. A systematic method for integrating a hydrogen management strategy within a rigorous refinery planning model is undertaken. The presented model consists of two main building blocks: a set of non-linear processing units’ models and a hydrogen balance framework. The two blocks are integrated to produce a refinery-wide planning model with hydrogen management. The hydrogen management alternatives were determined by economic analysis. The proposed model improves the hidden hydrogen unavailability that prevents refineries from achieving their maximum production and profit. The model is illustrated on representative case studies and the results are discussed. It was found that an additional annual profit equivalent to

Adaptive KPCA Modeling of Nonlinear Systems

7 million could be achieved with a one-time investment of

Corporate dashboards for integrated business and engineering decisions in oil refineries: An agent-based approach

13 million in a new purification unit.

Integrated multi-objective robust optimization and sensitivity analysis with irreducible and reducible interval uncertainty

This paper proposes an adaptive algorithm for kernel principal component analysis (KPCA). Compared to existing work: (i) the proposed algorithm does not rely on assumptions, (ii) combines the up- and downdating step to become a single operation, (iii) the adaptation of the eigendecompsition can, computationally, reduce to O(N) and (iv) the proposed algorithm is more accurate. To demonstrate these benefits, the proposed adaptive KPCA, or AKPCA, algorithm is contrasted with existing work in terms of accuracy and efficiency. The article finally presents an application to an industrial data set showing that the adaptive algorithm allows modeling time-varying and non-stationary process behavior.

OVERALL INTEGRATION OF THE MANAGEMENT OF H2 AND CO2 WITHIN REFINERY PLANNING USING RIGOROUS PROCESS MODELS

It is generally very challenging for an oil refinery to make integrated decisions encompassing multiple functions based on a traditional Decision Support System (DSS), given the complexity and interactions of various decisions. To overcome this limitation, we propose an integrated DSS framework by combining both business and engineering systems with a dashboard. The dashboard serves as a human-computer interface and allows a decision maker to adjust decision variables and exchange information with the DSS. The proposed framework provides a two-stage decision making mechanism based on optimization and agent-based models. Under the proposed DSS, the decision maker decides on the values of a subset of decision variables. These values, or the first-stage decision, are forwarded through the dashboard to the DSS. For the given set of first-stage decision variables, a multi-objective robust optimization problem, based on an integrated business and engineering simulation model, is solved to obtain the values for a set of second-stage decision variables. The two-stage decision making process iterates until a convergence is achieved. A simple oil refinery case study with an example dashboard demonstrates the applicability of the integrated DSS.

Improving Multi-Objective Robust Optimization Under Interval Uncertainty Using Worst Possible Point Constraint Cuts

Uncertainty considered in robust optimization is usually treated as irreducible since it is not reduced during an optimization procedure. In contrast, uncertainty considered in sensitivity analysis is treated as partially or fully reducible in order to quantify the effect of input uncertainty on the outputs of the system. Considering this, and the usual existence of both reducible and irreducible uncertainty, an approach that can perform robust optimization and sensitivity analysis simultaneously is of much interest. This article presents such an integrated optimization model that can be used for both robust optimization and sensitivity analysis for problems that have irreducible and reducible interval uncertainty, multiple objective functions and mixed continuous-discrete design variables. The proposed model is demonstrated by two engineering examples with differing complexity to demonstrate its applicability.

Sequential synthesis of heat integrated water networks: A new approach and its application to small and medium sized examples

New CO2 legislation forces the petroleum refining industry to review its operations and processes to cope with the new limitations of allowable CO2 emissions. Simultaneously, petroleum refineries, which are extremely complex entities, face another challenge represented by clean fuel products (low sulfur content) regulations. In an attempt to provide operational solutions to these issues, a CO2 management model was incorporated with an existing hydrogen management model that we have recently developed. To this end, this article presents an overall integrated model that solves simultaneously the refinery planning, hydrogen, and CO2 management problems. It addresses the optimum CO2 strategy selection through integration of refinery planning with the hydrogen network and CO2 emissions. The overall model was formulated as a mixed integer nonlinear program (MINLP). The model consists of the refinery emission sources and the considered mitigation options. Model performance was tested through different case studies with various reduction targets. The optimization results showed that the integration of the planning, hydrogen, and CO2 models lead to better profit margins and that CO2 mitigation options worked successfully together to meet a given reduction target. The obtained results also showed that the load shifting option can contribute up to a 3% reduction of CO2 emissions, while the fuel switching option can provide a 20% reduction. To achieve greater than 30% reductions, a CO2 capture technology must be employed in the petroleum refining industry.