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Plant-wide oscillation detection is an important task in the maintenance of large-scale industrial control systems, owing to the fact that in an interactive multi-loop environment oscillation generated in one loop may propagate to the different parts of the plant. In such a scenario, it is required that different loops oscillating due to a common cause and hence similar frequency may be grouped together. In this paper an adaptive method for plant-wide oscillation detection based on multivariate empirical mode decomposition (MEMD) along with a grouping algorithm is proposed. The method can identify multiple oscillation groups among different variables as well as variables with random noise only. The proposed method is also applicable to both non-linear and non-stationary time series where the techniques based on the conventional Fourier analysis are prone to errors. Within each group that oscillate due to a common cause, the method can also indicate the location of the probable root cause of oscillations. The efficacy of the proposed method is established with the help of both simulation and industrial case studies. [ABSTRACT FROM AUTHOR]

In this paper, an integrated coal gasification combined cycle system for the production of hydrogen and electricity is optimized in terms of energy and exergy efficiencies, and the amount and cost of the produced hydrogen and electricity. The integrated system is optimized by focusing on the conversion process of coal to syngas. A novel optimization process is developed which integrates an artificial neural network with a genetic algorithm. The gasification system is modeled and simulated with Aspen Plus for large ranges of operating conditions, where the artificial neural network method is used to represent the simulation results mathematically. The mathematical model is then optimized using a genetic algorithm method. The optimization demonstrates that the lower is the grade of coal of the three considered coals, the less expensive is the hydrogen and electricity that can be produced by the considered integrated gasification combined cycle (IGCC) system. [ABSTRACT FROM AUTHOR]

In this paper, we examine the mathematical structure of chemical reaction networks with the goals of identifying reaction invariant states and determining their physical significance. A combined species-reaction graph/convex analysis approach is developed to find semi-positive invariant states associated with a reaction network. Application of this graphical/algebraic reaction network analysis approach to four different chemical processes reveals that reaction invariants can represent conserved quantities other than elemental balances. [ABSTRACT FROM AUTHOR]

Artificial gas-lift (AGL) technique is commonly used to enhance oil production when the reservoir pressure in wells is not enough to sustain acceptable oil flow rate. However, the gas-lift wells are prone to instability, characterized by regular oscillations of pressure and flow. This phenomenon is known as casing-heading instability. It results in production loss and negative impact on downstream equipment, and has been a challenging problem to both industry and academia. In this paper, a novel concept of optimal tracking control is proposed for stabilization and operating mode transition in gas-lift wells when casing-heading phenomenon occurs. The stability of artificial gas-lift process is ensured by manipulating both gas lift choke and oil production choke, where the openings of both choke valves can vary from fully closed to fully open. Through the simulation of the open-loop system, a stability map of AGL process is produced. Then a trajectory optimization algorithm is developed based on this stability map, which is synthesized with a tracking controller to achieve trajectory optimization control. Also, a nonlinear state observer is designed to ensure estimation of unmeasurable variables. Through simulation studies, the effectiveness of proposed trajectory optimization control is demonstrated. [ABSTRACT FROM AUTHOR]

Highlights • We consider Infeasibility resolution for multi-purpose batch process scheduling. • We present a systematic approach for infeasibility diagnosis. • We exploit the known structure of scheduling models to isolate interpretable infeasible sets of constraints. • We demonstrate the power of the algorithm on infeasible instances of the Westenberger–Kallrath multipurpose batch process. Abstract Scheduling decisions give rise to some of the most challenging optimization problems in the process industry. Formulating mathematical models for scheduling problems and devising tailored solution algorithms for these models has been the main thrust of previous literature. In this work, we focus on analyzing and resolving the cause of bottlenecks and infeasibilities in generic scheduling problems. We present a systematic approach for infeasibility diagnosis. Our approach exploits the known structure of scheduling models to isolate interpretable infeasible sets of constraints. We demonstrate the power of the algorithm on infeasible instances of the Westenberger–Kallrath multipurpose batch process modeled using a state-task network (STN) representation. The methodology presented in the paper is able to successfully analyze the cause of infeasibility and provide recommendations for resolving it. We also demonstrate how these insights and recommendations can be presented to scheduling operators with little or no optimization expertise in an intuitive manner. [ABSTRACT FROM AUTHOR]

Highlights • A two phase algorithm for constrained black-box problems is presented. • It does not require a feasible initial point and can handle hard constraints. • Performance compared to NOMAD and COBYLA by applying on large set of problems. • Applied to optimize a process involving nonlinear algebraic and partial differential equations. Abstract This paper presents an algorithm for constrained black-box and grey-box optimization. It is based on surrogate models developed using input-output data in a trust-region framework. Unlike many current methods, the proposed approach does not require feasible initial point and can handle hard constraints via a novel optimization-based constrained sampling scheme. A two-phase strategy is employed, where the first phase involves finding feasible point through minimizing a smooth constraint violation function (feasibility phase). The second phase improves the objective in the feasible region using the solution of the feasibility phase as starting point (optimization phase). The method is applied to solve 92 test problems and the performance is compared with established derivative-free solvers. The two-phase algorithm outperforms these solvers in terms of number of problems solved and number of samples used. We also apply the algorithm to solve a chemical process design problem involving highly-coupled, nonlinear algebraic and partial differential equations. [ABSTRACT FROM AUTHOR]

This paper presents a design framework to modify the bifurcation characteristics and stability of high-dimensional, nonlinear, chemical processes within specified operating regions. Portions of solution branches are stabilized and oscillatory process dynamics in the vicinity of Hopf bifurcation points (HBPs) are attenuated. A new optimization algorithm is introduced to circumvent the limitations of traditional washout filters for feedback control. This controller is used to operate a high-dimensional, nitroxide-mediated, radical polymerization (NMRP) in a continuous-stirred-tank reactor (CSTR). The algorithm adjusts the eigenvalues of the model Jacobian matrix to relocate the HBPs. It is shown to permit flexible modifications of the bifurcation characteristics, providing acceptable performance for set-point tracking and disturbance rejection, and stabilizing solution branches in specified regions. [ABSTRACT FROM AUTHOR]

• In this paper, Erratum for a previously published article is presented. • Now, we see that the algorithm reaches the true optimum solution closely and is faster by 20% to 45% than the Kelley's cutting plane algorithm. • The corrections are reported. In Inamdar et al. (2011), the authors had mentioned about doing gradient calculations and weights multiplying the selected farthest point to compute a cutting plane. There was an error in the procedure that the gradients were calculated after multiplication by weight, whereas gradients are to be calculated using the farthest point set prior to multiplying by weights. Now, after making the necessary changes the answers obtained by Kelley's algorithm and sharp cut algorithm are the same except the latter converges about 20 % to 45 % faster than the former. The correction is reported here. [ABSTRACT FROM AUTHOR]