Real-time Optimization System Research Articles

Realtime optimization and management system (ROAM): A decision support system for digital agriculture systems

The growing disparity between food supply and demand requires innovative Digital Agriculture (DA) systems to increase farm sustainability and profitability. However, current systems suffer from problems of complexity stemming from the challenge of integrating diverse, often non-interoperable hardware and software components. In order to tackle these complexities to increase farm efficiency and understand the tradeoffs of these new DA innovations we developed Realtime Optimization and Management System (ROAM), which is a decision-support system developed to find a Pareto optimal architectural design to build DA systems. To find the Pareto optimal solution, we employed the Rhodium Multi-Objective Evolutionary Algorithm (MOEA), which systematically evaluates the trade-offs in DA system designs. Based on data from five live deployments at Cornell University, each DA design can be analyzed based on user defined objectives and evaluated under uncertain farming environments with ROAM. Paired with this, we develop a web interface that allows users to define personalized decision spaces and visualize decision tradeoffs. To help validate ROAM, it was deployed to a commercial farm where the user was recommended a DA architecture design method to increase farm efficiency. ROAM allows users to quickly make key decisions in designing their DA systems to increase farm profitability.

Assessing the value of seasonal hydrological forecasts for improving water resource management: insights from a pilot application in the UK

Abstract. Improved skill of long-range weather forecasts has motivated an increasing effort towards developing seasonal hydrological forecasting systems across Europe. Among other purposes, such forecasting systems are expected to support better water management decisions. In this paper we evaluate the potential use of a real-time optimization system (RTOS) informed by seasonal forecasts in a water supply system in the UK. For this purpose, we simulate the performances of the RTOS fed by ECMWF seasonal forecasting systems (SEAS5) over the past 10 years, and we compare them to a benchmark operation that mimics the common practices for reservoir operation in the UK. We also attempt to link the improvement of system performances, i.e. the forecast value, to the forecast skill (measured by the mean error and the continuous ranked probability skill score) as well as to the bias correction of the meteorological forcing, the decision maker priorities, the hydrological conditions and the forecast ensemble size. We find that in particular the decision maker priorities and the hydrological conditions exert a strong influence on the forecast skill–value relationship. For the (realistic) scenario where the decision maker prioritizes the water resource availability over energy cost reductions, we identify clear operational benefits from using seasonal forecasts, provided that forecast uncertainty is explicitly considered by optimizing against an ensemble of 25 equiprobable forecasts. These operational benefits are also observed when the ensemble size is reduced up to a certain limit. However, when comparing the use of ECMWF-SEAS5 products to ensemble streamflow prediction (ESP), which is more easily derived from historical weather data, we find that ESP remains a hard-to-beat reference, not only in terms of skill but also in terms of value.

Economic Control Structure Selection for Two-Layered Real-Time Optimization Systems

In industrial chemical plants, the selection of controlled variables, manipulated variables, and setpoint values directly impacts the economics of plant operation. The economic operation of a contr...

Real-Time Sensing and Control of Integrative Horticultural Lighting Systems

Optical radiation, including light, plays a crucial role in the structural development of plants through photomorphogenesis and the response to environmental changes. However, plant sensitivity to optical radiation widely varies across species. While research efforts are currently underway to discover the fundamentals of plant physiology, light sources with preprogrammed light settings (light recipes) are offered to clients to expedite plant growth. Since horticultural lighting research is in its infancy, prescribed lighting conditions are not likely to address every plants’ needs in terms of the spatial and spectral distribution, intensity, and duration of the light sources. However, it is possible to imagine an intelligent horticultural lighting system that can diagnose plants through sensors, and adjust the light intensity, the spatial and spectral distribution for the specific plant species with active feedback. Such an advanced real-time horticultural lighting system would consist of sensors to detect physiological markers from plants and environmental factors and an artificial intelligence algorithm to adjust the output. While the underlying technology for a real-time optimization system exists, the implementation and training would require further research.

A Bilevel Programming Formulation for Dynamic Real-time Optimization

Due to ever-challenging global market conditions, plant economic optimization is becoming more critical. Recent advances have transformed the traditional steady-state real-time optimization (RTO) system of plant economic optimization to dynamic real-time optimization (DRTO) based on a dynamic prediction model. DRTO strategies that have been proposed perform economic optimization in an open-loop fashion without taking into account the presence of the plant control system. In this work, we propose a bilevel programming formulation for DRTO that includes effects of the closed-loop dynamics of an underlying constrained model predictive controller (MPC). The bilevel program is subsequently reformulated and solved as a single-level mathematical program with complementarity constraints (MPCC). We investigate the economics and control performance of the proposed strategy under varying MPC controller design parameters, and compare them to open-loop DRTO and rigorous multilevel closed-loop DRTO approaches.

A short overview of control in European R&D programmes (1983–2013): From local loop designs, through networked and coordinated control, to stochastic, large scale and real time optimization systems

The main purpose of this article is to summarize the activities related to control and control systems supported by the European Commission Research and Development (R&D) programmes (so called Framework Programmes—FPs) with focus on the last two decades and to present and discuss the synergies, gaps and possible new challenges identified during several brainstorming and informal meetings and in related reports and position papers covering topics from loop performance improvements to distributed, stochastic and large scale systems of systems.A second goal is to serve as a small additional source of systems and control information, listing examples of funded projects and recommendations for future work grouped according to established control categories and the EC themes and areas in the published work programmes.This note also marks 12 years after the European Journal of Control special issue, back in 2001, which was dedicated to the results of a pioneering Verification of Hybrid Systems project (VHS) [60]. About the same time a special EC project, Awareness and Dissemination Activities for Advanced Control In Europe (ADACIE), supported three major control events i.e. IFAC in Barcelona, MED in Lisbon and ECC in Porto and Cambridge [1].This is a collection of factual and personal reflections of the author, based on several years of following up public and industrial activities in systems and control. The qualitative interpretation of the project results and discussions presented however have to be complemented by the actual and detailed scientific and technological deliverables and papers published by the control teams involved. A small set of useful references is included.Open questions still remain and novel concepts and ideas may become part of new endeavors of the European and international communities in funded activities, or in independent work. Furthermore the reported activities also indicated that there are more unexplored synergies among these disciplines (e.g. control, computing, communications, cognition, complexity and mathematics), which would necessitate new ways of open and joint efforts.

Integrating dynamic economic optimization and model predictive control for optimal operation of nonlinear process systems

In this work, we propose a conceptual framework for integrating dynamic economic optimization and model predictive control (MPC) for optimal operation of nonlinear process systems. First, we introduce the proposed two-layer integrated framework. The upper layer, consisting of an economic MPC (EMPC) system that receives state feedback and time-dependent economic information, computes economically optimal time-varying operating trajectories for the process by optimizing a time-dependent economic cost function over a finite prediction horizon subject to a nonlinear dynamic process model. The lower feedback control layer may utilize conventional MPC schemes or even classical control to compute feedback control actions that force the process state to track the time-varying operating trajectories computed by the upper layer EMPC. Such a framework takes advantage of the EMPC ability to compute optimal process time-varying operating policies using a dynamic process model instead of a steady-state model, and the incorporation of suitable constraints on the EMPC allows calculating operating process state trajectories that can be tracked by the control layer. Second, we prove practical closed-loop stability including an explicit characterization of the closed-loop stability region. Finally, we demonstrate through extensive simulations using a chemical process model that the proposed framework can both (1) achieve stability and (2) lead to improved economic closed-loop performance compared to real-time optimization (RTO) systems using steady-state models.

Solving dynamic optimization infeasibility problems

Dynamic real-time optimization (DRTO) systems sometimes fail when solving intrinsic optimization problems. There are situations where the solution is infeasible due to the initial conditions, constraint changes during operation, or even the presence of conflicts on constraint specifications. By using a goal programming approach, this work proposes a method to solve these infeasibilities by reformulating the differential-algebraic optimization problem as a multi-objective dynamic optimization problem with path constraint relaxations. Three examples were solved exploring the characteristics of such infeasibility problems. The results demonstrate the ability of the proposed method in identifying and relaxing the constraint violations, increasing the robustness of DRTO systems.

Monitoring and adaptive control of CO2 laser flame cutting

In this paper a real-time control and optimization system for laser flame cutting of thick plates of mild steel is presented. The proposed system consists of two subsystems, namely a process monitoring and a control and optimization module.The process monitoring module evaluates the cut quality by measuring a set of sensing parameters, which are well-correlated with different quality characteristics of the cut surface. The applicability of different optical sensors (photodiodes and a NIRcamera) has been investigated. An overview of the most suitable set of sensing parameters is presented. The correlations between different quality deteriorations and the sensing parameters are mentioned as well as the reasons for these correlations.The real-time control and optimization module is implemented as an expert system with a dual functionality. On the one hand, it compares the sensing parameters with predefined thresholds and assigns one of the predefined quality classes to the instantaneous cut quality. On the other hand, it modifies the cutting parameters based on the predefined set of interpretable rules corresponding to the identified quality class.The obtained results prove the effectiveness of the chosen approach in terms of increased autonomy, productivity, and efficiency of the process, as well as elimination of the need for manual quality control and the possibility to automatically generate quality reports.

Passivity-based control of the float-glass process

Passivity-based control of mass and energy inventory has been used for about a decade in selected PPG applications. The methods described in this article are now being implemented throughout the PPG system with excellent operator acceptance. The passivity-based methodology has been applied to other processes, including chemical vapor deposition (CVD), a silicon reactor, fluid-bed control and automotive windshield bending. The main challenges are to develop real-time optimization systems to speed up product changeover, maximize energy efficiency (currently at less than 50% of theoretical achievable in a regenerator furnace), extend furnace life, and increase production rate, it is necessary to develop methods for model reduction and multi-scale optimization that can be implemented in real time. These methods must be flexible and easy to commission and maintain in order to facilitate broad application in the manufacturing industries

Real Time Optimization: Classification and Assessment

Summary The Real-Time Optimization (RTO) Technical Interest Group (TIG) has endeavored to clarify the value of real-time optimization projects. RTO projects involve three critical components: People, Process, and Technology. Understanding these components will help establish a framework for determining the value of RTO projects. In this paper, the Technology component is closely examined and categorized. Levels within each Technology category are illustrated by use of spider diagrams, which help decision makers understand the current status of operations and the future RTO status. The perception of uncertain value has been one of the critical issues in adopting RTO systems in our industry. Therefore, case histories are reviewed to demonstrate the impact of RTO projects. To assist RTO project promotion further, we list lessons learned, suggest a justification process, and present a simple example of an economic-evaluation process. Introduction Industry case histories demonstrate many types of benefits from RTO such as production-volume increase; better return on investment (ROI); higher decision quality; health, safety, and environment (HSE) improvements; and operational expenditures (OPEX) reduction. However, they have lacked systematic project-evaluation processes for justification. Today, promoting RTO is, in essence, a competition for capital within a company. The project teams that recognize this fact and then clearly outline the purpose, benefits, costs (direct or indirect), and strategic business alignment of their proposals will be in an advantageous position to secure funding. Because RTO is still an emerging discipline, classifying projects of this nature is still dependent on an individual's point of view. This paper provides classification of RTO to help provide a common vocabulary to address a multitude of issues.

Advanced practical process control, B. Roffel and B. H. L. Betlem, Springer, Berlin, Germany, 2004 309 pp, Price 85.55€

Advanced practical process control, B. Roffel and B. H. L. Betlem, Springer, Berlin, Germany, 2004 309 pp, Price 85.55€

Implementation of on-line performance monitoring system at Seoincheon and Sinincheon combined cycle power plant

As a result of the restructuring of the Korean Power Generation Industry, Korean power companies are under increased pressure to increase capacity and lower operating costs. In order to achieve these goals, an on-line real-time performance monitoring system has been introduced to the Seoincheon and Sinincheon combined cycle power plants (CCPP). The on-line real-time performance monitoring and optimization system helps plant personnel operate the plants profitably by tracking plant performance changes based on real-time information. This paper outlines how the on-line performance monitoring system is being used to improve the operational efficiency and optimize equipment maintenance. Case studies showing the benefits provided by the system are presented. This system will be useful for the plants to respond to the competitive electricity market.

Real‐time Optimization of Acetaldehyde Production Process

AbstractA real‐time optimization (RTO) system of an acetaldehyde production process is presented in this paper. Data reconciliation with steady state detection and gross error detection, soft sensoring of coarse acetaldehyde concentration, formulation and implementation of yield optimization are discussed in detail. The effect of the RTO system described was validated in an industrial chemical plant, with an average 2.68% increase in yield promotion obtained, which confirms the effectiveness and practicability of the proposed technique.

Optimal Sensor Selection for Successful Real-Time Optimization

In Real-Time Optimization (RTO) systems, the perfomance of the RTO depends strongly on the selection of measured variables and of sensors used for their measurements. Many studies have investigated the selection of variables (e.g., Krishnan, et al., 1992; Fraleigh, et al., 1998). This study extends previous work by considering the error in the implementation of the RTO results due to imperfect sensors used in process control. A practical optimal sensor design strategy based on nonlinear closed-loop Monte-Carlo simulations is developed. Case study results demonstrate that the implementation error can be essential for determining the appropriate sensors

Measurement Noise Tolerance and Results Analysis for Iterative Feedback Steady-State Optimization

A robust model-based iterative feedback optimization methodology for the steady-state optimization of chemical process operations was introduced in a previous work (Cheng, J.-H.; Zafiriou, E. Ind. Eng. Chem. Res. 2000, 39, 4215) as a complementary approach to conventional real-time optimization (RTO) methods to improve plant operations without requiring cumbersome model updating. In this paper, the results analysis component that is an integral part of an RTO system is developed for use with this methodology to evaluate the inherent variability of the optimization results transmitted from the plant measurements. In this way, the method is able to distinguish the effect of model−plant mismatch from that of measurement noise, and only optimization results that represent meaningful changes are implemented as the new set points, thus reducing unnecessary and profitless corrective actions. Furthermore, the iterative nature of the methodology allows for the development of a condition for guaranteed improvement over iterations, which can be used to quantify, a priori, the limits that measurement noise imposes on achieving optimal operation. The effectiveness of the method is demonstrated on a simulated CSTR process.

Results Analysis in a Constrained Real-Time Optimization (RTO) System

This paper presents a new results analysis strategy for uncertain real-time optimization (RTO) systems. The key contributions of this paper are in I) developing a mathematical formulation describing the confidence region of a partially constrained optimum and 2) applying that formulation in deciding whether the optimizer results should be implemented. The confidence region of the constrained optimum is determined from the confidence region of the unconstrained optimum, when the inequality constraints are not considered. The confidence region of the unconstrained optimum is mapped to the feasible region defined by the inequality constraints to obtain the confidence region of the constrained optimum. This mapping is developed by minimizing the profit loss between the unconstrained and constrained optima. The resulting confidence region of the constrained optimum is used in optimization results analysis; the confidence regions of successive predicted optima are compared to decide if the difference between two optima is statistically significant. The comparison is made by calculating the significance level at which the two confidence regions just overlap. If the value is small, large portions of two 95% confidence regions overlap, and the optimizer result will not be implemented. The new results analysis approach is applied to the Williams-Otto reactor case study with controlled inequality constraints, where it significantly reduced plant variability, while concurrently increasing the operating profit.

Real-Time Optimization of Distillation Column via Sliding Modes

Abstract The real-time optimization (RTO) system of distillation column has been proposed using sliding modes. On the basis of formulated optimization problem, the parameters for the distillate composition controller are so selected that the condition for the occurrence of sliding modes holds in order to provide a search procedure. An investigation of the transient performance of RTO-system under feed composition disturbances indicated a stable tracking for the shifted optimal distillation operating points.

The effect of model fidelity on real-time optimization performance

The ability of a real-time optimization (RTO) system to track the changing optimum closely relies on an accurate model for representing the plant behavior. This paper investigates the effect of model fidelity on RTO performance using a simulated industrial boiler network case study. Three optimization approaches with very different modeling fidelity are investigated: (1) model-free direct search method; (2) model-based method using a simplified efficiency curve model; and (3) model-based method using a fundamental model. The model-free direct search method builds a locally linear model using plant data. It takes many steps to reach the optimum, which causes a significant profit loss during tracking. This tracking loss can be reduced by using the model-based RTO system. The RTO system with an updated, detailed fundamental model is able to track fast and large disturbances because the model is accurate in a large range of operation. The RTO system with a simplified efficiency model requires periodic experimentation to correct for the disturbances, which can cause a significant profit loss during experimentation and tracking. This study demonstrates how quantitative performance measures improve as higher fidelity models are used in real-time operations optimization.

Sensor selection for model-based real-time optimization: relating design of experiments and design cost

Abstract Real-time optimization systems have become a common tool, in the continuous manufacturing industries, for improving process performance. Typically, these are on-line, steady-state, model-based optimization systems, whose effectiveness depends on a large number of design decisions. The work presented here addresses one of these design decisions and proposes a systematic approach to the selection of sensors to be used by the RTO system. This paper develops a sensor system selection metric based on a trade-off between two approaches to the design of experiments, which is shown to be consistent with the design cost approach of Forbes and Marlin [Computers Chem Eng 20 (1996) 7/7]. The resulting design metric is incorporated into a systematic procedure for RTO sensor selection problem. Finally, the proposed RTO sensor selection procedure is illustrated with a case study using the Williams–Otto [AIEE Trans 79 (1960), 458] plant.