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Hybrid gas turbine–fuel cell systems have immense potential for high efficiency in electrical power generation with cleaner emissions compared with fossil-fueled power generation. A systematic cont...

Selection of primary controlled variables (CVs) with due consideration of economic loss and ease of control is an important step for achieving optimal plant operation under design and off-design conditions. However, the computational expense of the combinatorial optimization step that is undertaken for CV selection renders the existing approaches unaffordable for fast selection of optimal CVs. Two approaches are proposed in this paper for improving the computational efficiency of the optimization step. Typically, the CV selection process is typically executed by considering the entire plant together. Opposed to that, the first approach proposed here seeks to decompose the process plant into multiple partitions based on the connectivity strength. Then the CV selection algorithm can be executed on each partition independently. The decomposition algorithm is biomimetic and has been developed, in analogy to neuroscience, mimicking how the information about the connectivity information is extracted using a dynamic casual model. This approach can significantly decrease the number of combinatorial optimization problems that needs to be solved and facilitates parallelization of the solution. In the second approach, a multiagent optimization framework is proposed that utilizes metaheuristic optimization strategies such as the efficient ant colony algorithm, simulated annealing and the genetic algorithm. These two approaches can be used by themselves or in combination. This framework enables cooperative search by a group of algorithmic agents facilitated through an information sharing protocol. These two approaches are applied to a toy problem and an acid gas removal unit as part of an integrated gasification combined cycle. The study shows how the computational time and economic and control performance of the optimal CV sets vary as the number of partitions are changed.

In this paper, a novel framework is proposed for integrating biomimetic-based advanced control and multi-agent optimization approaches for nonlinear chemical process applications. In particular, a Biologically-Inspired Optimal Control Strategy, denoted as BIO-CS, is combined with multi-agent optimization (MAO) techniques to provide optimal solutions for dynamic systems. In this combined framework, the BIO-CS algorithm employs gradient-based optimal control solvers for the intermediate problems associated with the leader-follower agents’ local interactions. Also, the MAO uses the capabilities of heuristic-based optimization techniques by sharing process information to obtain optimal operating setpoints for the controller considering an overall process objective. The applicability of the proposed method is demonstrated using a nonlinear, multivariable, process model of a fermentation system. Specifically, the optimal operating points are computed by the MAO implementation for setpoint tracking, trajectory tracking and plant-model mismatch scenarios for BIO-CS application. Results of the developed framework are compared to a gradient-based Sequential Quadratic Programming (SQP) technique and a classical proportional-integral (PI) controller in terms of optimization and control studies, respectively. As an additional contribution, BIO-CS is also cast as a model predictive controller (MPC) for the first time and compared to the agent-based BIO-CS approach in terms of computational time and tracking error. Closed-loop control results show up to 46% improvement in tracking performance during transient for the multi-agent BIO-CS when compared to BIO-CS as MPC for additional computational expense. The obtained results illustrate the capabilities of this novel integrated framework including BIO-CS as MPC to achieve desired nonlinear system performance for various scenarios.

International audience

Heavy metals in drinking water act as contaminants that can cause serious health problems. These metal ions in drinking water are generally removed using cation exchange resins that are used as adsorbents. Generally, chelating resins with limited adsorption capacity are commercially available. Manufacturing novel resin polymers with enhanced adsorption capacity of metal ion requires ample experimental efforts that are expensive as well as time consuming. To overcome these difficulties, application of computer-aided molecular design (CAMD) will be an efficient way to develop novel chelating resin polymers. In this paper, CAMD based on group contribution method (GCM) has been used to design novel resins with enhanced adsorption capability of removing heavy metal ions from water. A polymer consists of multiple monomer units that repeat in a polymer chain. Each repeat unit of the polymer can be subdivided into different structural and functional groups. The adsorption mechanism of heavy metals on resin depends on the difference between activities in adsorbents and the bulk fluid phase. The contribution of the functional groups in the adsorption process is found by estimating the activity coefficient of heavy metal in the solid phase and bulk phase using a modified version of the UNIFAC GCM. The interaction parameters of the functional groups are first determined and then they are used in a combinatorial optimization method for CAMD of novel resin polymers. In this work, designs of novel resin polymers for the removal of Cu ions from drinking water are used as a case study. The proposed new polymer resin has an order of magnitude higher adsorption capacity compared to conventional resin used for the same purpose.

A bicriterion, multiperiod, stochastic mixed-integer linear programming model to address the optimal design of hydrocarbon biorefinery supply chains under supply and demand uncertainties is presented. The model accounts for multiple conversion technologies, feedstock seasonality and fluctuation, geographical diversity, biomass degradation, demand variation, government incentives, and risk management. The objective is simultaneous minimization of the expected annualized cost and the financial risk. The latter criterion is measured by conditional value-at-risk and downside risk. The model simultaneously determines the optimal network design, technology selection, capital investment, production planning, and logistics management decisions. Multicut L-shaped method is implemented to circumvent the computational burden of solving large scale problems. The proposed modeling framework and algorithm are illustrated through four case studies of hydrocarbon biorefinery supply chain for the State of Illinois. Comparisons between the deterministic and stochastic solutions, the different risk metrics, and two decomposition methods are discussed. The computational results show the effectiveness of the proposed strategy for optimal design of hydrocarbon biorefinery supply chain under the presence of uncertainties. © 2012 American Institute of Chemical Engineers AIChE J, 2012

In recent years, there has been a growing increase of the cooling demand in many parts of the world, which has led to major energy problems. In this context, solar assisted absorption cooling systems have emerged as a promising alternative to conventional vapor compression air conditioning systems, given the fact that in many cases the cooling demand coincide with the availability of solar radiation. In this work, we present a decision-support tool based on mathematical programming for the design of solar assisted absorption cooling systems. The design task is formulated as a bi-criteria mixed-integer nonlinear programming (MINLP) optimization problem that accounts for the minimization of the total cost of the cooling system and the associated environmental impact measured over its entire life cycle. The capabilities of the proposed method are illustrated in a case study that addresses the design of a solar assisted ammonia-water absorption cycle considering weather data of Barcelona (Spain).

In this paper, an efficient ant colony optimization (EACO) algorithm is proposed based on efficient sampling method for solving combinatorial, continuous and mixed-variable optimization problems. In EACO algorithm, Hammersley Sequence Sampling (HSS) is introduced to initialize the solution archive and to generate multidimensional random numbers. The capabilities of the proposed algorithm are illustrated through 9 benchmark problems. The results of the benchmark problems from EACO algorithm and the conventional ACO algorithm are compared. More than 99% of the results from the EACO show efficiency improvement and the computational efficiency improvement range from 3% to 71%. Thus, this new algorithm can be a useful tool for large-scale and wide range of optimization problems. Moreover, the performance of the EACO is also tested using the five variants of ant algorithms for combinatorial problems.

This work presents a multi-period and multi-objective optimization based on mathematical programming of solar assisted absorption cooling systems. Seven solar collector models combined with a gas fired heater and an absorption cooling cycle are considered. The optimization task is formulated as a multi-objective multi-period mixed-integer nonlinear programming (MINLP) problem that accounts for the minimization of the total cost of the cooling system and the associated environmental impact. The environmental performance is measured following the Life Cycle Assessment (LCA) principles. The capabilities of the proposed method are illustrated in a case study that addresses the design of a solar assisted ammonia-water absorption cooling system using the weather conditions of Tarragona (Spain).