Tight Operational Constraints Research Articles

Nonlinear model predictive control of ultra-high-purity air separation units using transient wave propagation model

Model reduction techniques can be used to reduce the computational burden associated with nonlinear model predictive control (NMPC). In our recent work, we introduced the transient nonlinear wave propagation model (TWPM) for reduced dynamic modeling of multi-component distillation columns with variable holdup, and demonstrated its suitability for optimization and control of single-section distillation columns and simple air separation units [Caspari et al., J. Process Control, 2020]. We show here that the TWPM is well-suited for reduced modeling of multi-sectional ultra-high-purity distillation columns and enables real-time capable NMPC of complex process flowsheets with tight operational constraints. To demonstrate its performance and accuracy, we apply the TWPM for NMPC of an ultra-high-purity nitrogen air separation unit. We perform an in-silico closed-loop case study comprising a series of load changes. Our approach reduces CPU time by 84%, enabling NMPC in real time.

Flexible Use of Residential Heat Pumps - Possibilities and Limits of Market Participation

The increased amount of electricity supply from intermittent renewable energy sources leads more and more to high price volatility in electricity spot markets. An increasing share of generation is less dispatchable than in the past, and therefore higher amounts of flexible demand, which can be adjusted towards supply, are required. Even residential consumers are potential market participants, if the smart equipment of buildings and the electricity grid are readily available. This paper investigates the possibility for heat-pump operators to participate in spot markets. Especially problems and possible benefits are investigated when uncertainties in ambient temperatures or prices are considered. Therefore an optimization model, including an air-to-water heat pump, a storage tank and the heated building is implemented in MATLAB. In order to investigate the heat-pumps operation according to optimized heat-supply schedules. Along different scenarios, an agent-based model is used. Namely operations with day-ahead and intraday-market participation are investigated, using historical EPEX spot electricity prices for 2014. Results show that uncertainty is a critical issue when private consumers participate in electricity markets. Even with a certain amount of system flexibility, there are tight operational constraints for the heating device, which are hard to fulfill. Short-term decisions including responses to current information are required. The system behavior is acceptable with very short-term decision making, namely a hourly reoptimization with intraday-market participation. Further on, benefits can be yielded, when a combination of procurement before (day-ahead) and adjustments in the very short term (intraday) are applied.

Advances in the simulation of offshore heavy lifts from experiences in Bass Strait, Victoria

There has always been a clear need to demonstrate that project critical offshore heavy lifts can be conducted safely, within allowable criteria, for the metocean conditions likely to occur in-field. Extensive guidance is available in the design codes for lifts subsea but, for lifts in air, dynamic analysis of the response of a suspended mass is not fully addressed. Analyses generally yield results that do not agree well with operating experience and cannot easily be assessed against the limited code guidance available. Traditionally, operator skill and experience has thus been relied on to justify the operability of such lifts. Recent experience in engineering the lift of a 500 tonne module onto the Yolla Platform in Bass Strait, Victoria, has further underlined the deficiencies in analytical techniques and approaches available to the industry. This paper presents experience gained from the pre- and post-lift analysis of an instrumented lift operation recently conducted by the mono-hull vessel Sapura 3000 in the challenging environmental conditions of Bass Strait. This analysis was used to demonstrate that the lift could be conducted safely within tight operational constraints, meeting the codified criteria set. Subsequent analysis of data gathered during the lift was used to explore and refine modelling assumptions to aid the engineering of future challenging heavy lifts. The analyses conducted advanced state-of-the-art work in this field for the offshore industry. By enabling numerical techniques for heavy lifts, which are typical of many other offshore operations, safety will be improved, project contingencies can be properly estimated through more accurate prediction of downtime, and cost savings can be achieved by the selection of more effective vessels for the task.

Increasing constructive engagement and positive affect for residents with severe and very severe dementia through group‐based activities

A group-based multisensory activity program (Sensory Day) for residents with dementia was developed, to address the challenge of providing personalised activities within tight operational constraints in residential aged care facilities. Fourteen participants with severe and very severe dementia were observed before, during and after participation in one of four Sensory Day sessions. The Menorah Park Rating Scale was used to yield four levels of engagement. The Philadelphia Geriatric Affect Rating Scale was used to identify four affect states. Dementia severity was ascertained by PAS-CIS scores mapped onto the Global Deterioration Scale. Increased levels of constructive engagement and positive affect were observed during participation in the Sensory Day sessions, relative to measures taken before the session. This novel approach to activity programming demonstrates that it is possible to provide group-based activities for residents with severe and very severe dementia which result in increased engagement and positive mood.

Decentralized Control of Solid Oxide Fuel Cells

Environmental concerns and limited availability of natural resources have made solid oxide fuel cells (SOFC) a promising alternative for large-scale electricity production. A control system is needed for SOFCs to meet operational objectives and ensure safe operation. Previous studies showed that the control of SOFC is challenging due to its slow response time, tight operating constraints and unavailability of some key measurements. In this work, simple yet reliable decentralized proportional-integral-derivative (PID) controllers are systematically designed based on a benchmark nonlinear dynamic model of SOFC . The design procedure involves selection of controlled variables (CVs), input-output pairing selection, and PID controller tuning. For CV selection, a novel method is proposed such that maintaining the CVs at constant setpoints facilitates constraint satisfaction for all key variables. Closed-loop simulations demonstrate that the proposed decentralized PI controllers are able to closely meet the control objectives of SOFC even in the presence of changing operating conditions.

Local Self-optimizing Control with Input and Output Constraints

AbstractThe available methods for selection of controlled variables (CVs) using the concept of self-optimizing control have been developed under the restrictive assumption that the set of active constraints remains unchanged for all the allowable disturbances. To keep the input and output variables within their allowable bounds, the use of cascade controllers, and to track the optimal set of active input constraints, the use of split-range controllers is suggested in literature. In this paper, we propose a different strategy, where CVs are selected as linear combinations of measurements to minimize local average loss, while ensuring that all the constraints are satisfied over the allowable set of disturbances. This result is extended to select a few of the available measurements, whose combinations are used as CVs. The proposed approach offers simpler implementation of operational policy for processes with tight operational constraints. We use the case study of forced-circulation evaporator to illustrate the usefulness of the proposed method.

Practical optimization of complex chemical processes with tight constraints

Stochastic optimization algorithms are introduced for complex chemical processes—using semicontinuous distillation with reaction in a middle vessel as a case study. The processes are dynamic, consisting of several operating modes, and have tight operating constraints to prevent weeping or flooding in the distillation column of the case study. Often intensification leads to fewer processing units, operated in many modes over a cyclic campaign, introducing tight operating constraints. The optimization algorithms examined include a modified univariate technique for N-dimensions, the particle swarm method, the simplex method, and others. These methods are used jointly in a bi-level algorithm, and are tested for speed and quality of result.

Mathematical Modeling and Optimization of a Semi-Batch Polymerization Reactor

Abstract This paper concerns mathematical modeling and optimization of a labscale and a pilot-scale semi-batch free-radical homo-polymerization reactors. A first-principles mathematical model is developed by using reaction rate laws available in polymerization literature. The model is validated by comparing the model predictions of monomer conversion, polymer mass fraction, number-average molecular weight, weight-average molecular weight, and polydispersity index to off-line measurements taken in pilot-scale and lab-scale polymerization reactors at DuPont’s Marshall Laboratory. The validated model is then used to calculate optimal initiator and monomer feed-rate policies which minimize the polydispersity index of the final polymer product, subject to a set of tight operational constraints.