Plant-wide Optimization Research Articles

Data-based multiple-model prediction of the production rate for hematite ore beneficiation process

The prediction of the production rate of the hematite ore beneficiation process is important to plant-wide optimization. This paper presents a data-based multi-model approach to predict the production rate with multiple operating modes. The inputs of the predictive model are the performance indices of each unit process, and the output is the global production index (the production rate) of the hematite ore beneficiation process. The multiple models are developed by integrating the fuzzy clustering algorithm and machine learning algorithm. A global model, Takagi–Sugeno–Kang fuzzy model, and multiple neural network model were compared using the data obtained from a practical industrial process, and the effectiveness of the proposed algorithm was proven.

Real-time optimization based on SCFO for gold cyanidation leaching process

The real time optimization (RTO) of gold cyanidation leaching process has been investigated in this paper. To solve inevitable plant-model mismatch and process disturbance in practice, an RTO strategy based on SCFO (Sufficient Conditions for Feasibility and Optimality) for gold cyanidation leaching process is proposed, where the SCFO are used to enforce the desirable properties (both feasibility and optimality) of the solutions resulted from the traditional RTO methods. The simulation results show that compared with the traditional RTO methods, there is a significant improvement of production cost by enforcing the SCFO under model-parameter uncertainty, model-structure uncertainty and unknown process disturbance, which has laid an important foundation for the successful implementation of the plant-wide optimization and control for hydrometallurgy process.

Intelligent Motor Control: Innovations in Process Optimization

As technology continues to drive innovations, industrial enterprises must keep pace to remain competitive in an ever-changing marketplace. The technology trend is to replace the systems of yesterday with higher-performance, lower-cost, option-rich devices that shorten the return on investment and offer more flexibility. As control system technology evolves, systems migrate to functions that are being increasingly distributed to smarter, more granular control system components capable of performing localized operations. These functions were once the responsibility of a central or master controller. For example, there is integration of intelligent devices, device-level networks, and interrogation software into medium-voltage (MV) and lowvoltage (LV) motor-control centers (MCCs). Technology can deliver improved diagnostics, early warnings for increased system reliability, design flexibility, simplified wiring, and another level of personnel safety for process industries. This article focuses on technology integration, intelligent devices, and device-level information collection to provide convergence of control and information for plantwide optimization.

Plant-wide Optimization of a Full-Scale Activated Sludge Plant with Anaerobic Sludge Treatment

This paper presents the application of a plant-wide model-based methodology to wastewater treatment plants. The focus is on a tertiary activated sludge plant with anaerobic sludge treatment, owned and operated by Sydney Water. A dynamic plant-wide model is first developed and calibrated using historical data. A scenario-based optimization procedure is then applied for computing the effect of key discharge constraints on the minimal net power consumption, via the repeated solution of a dynamic optimization problem. The results show a potential for reduction of the energy consumption by about 20%, through operational changes only, without compromising the current effluent quality. It is also found that nitrate (and hence total nitrogen) discharge could be reduced from its current level around 22 mg(N)/L to less than 15 mg(N)/L with no increase in net power consumption, and could be further reduced to <10 mg(N)/L subject to a 15% increase in net power consumption upon diverting part of the primary sludge to the secondary treatment stage. This improved understanding of the relationship between nutrient removal and energy use will feed into discussions with environmental regulators regarding nutrient discharge licensing.

Energy and Emissions Optimisation at Chevron Cape Town

Across the hydrocarbon processing industry today, energy and utility costs are often the largest operating expense after the purchase of raw materials. Performance of the plant fuel gas systems heavily impacts variable fuel costs, environmental emissions (like SOx and NOx release), overall complex performance and throughput. Controlling fuel gas header pressure and quality introduces the complexities of non-linear dynamics, significant self-propagating interaction and insufficient degrees of freedom to solve the combined control and optimization problem.Chevron has installed a system to manage these complexities on their 90 000 bpd refinery in Cape Town, South Africa. The system, designed and installed by BluESP, performs plant-wide optimisation across three vaporisers in different parts of the plant and four boilers in the utilities section. The solution uses linear model predictive control with gain scheduling and an LP optimiser to operate against sulphur dioxide emission limits as well as hydraulic constraints. This paper discusses the challenges in controlling the system, the use of model predictive control to address these challenges, as well as the benefits achieved.

Raising the Decision-Making Level to Improve the Enterprise-Wide Production Flexibility

When market demand significantly changes, as in the ongoing worldwide economic crisis, many production plants are forced to operate far from nominal conditions. In this case, the current plant-wide optimization of production sites is a myopic approach that could lead to plant inefficiencies and unconventional operation issues, thus, resulting in ineffective prevention of economic losses. A way to tackle low-demand conditions is to raise the decision-making process from the plant-wide (or business-wide) level to the enterprise-wide (or corporate) level by assigning a Boolean variable to each production site so as to manage their on/off status. By doing so, certain additional (social) constraints may become relevant. The case of operating industrial gases supply chains is considered. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1588–1598, 2013

Process Goose Queue Methodologies with Applications in Plant-wide Process Optimization

Inspired by biologic nature of flying wild geese, a so-called process goose queue (PGQ) technique oriented for plant-wide optimization is established. Taking advantage of this ad-hoc structure of flying geese, a plant-wide process can be decomposed into several hierarchically connected PGQs along the direction of the objective function generation. In line with this thought, plant-wide process optimization is accordingly identical with the following and tracking issues between leading and following geese. Followed by this philosophy, related theoretical definitions and modeling principles together with enabling algorithms are explicitly introduced. With the characteristics of evolutionary optimization, PGQ approach is able to overcome the algorithmic deficiencies associated with conventional optimizations. To demonstrate the feasibility and validity of the contributions, TE process is employed as the case study.

Offline Modeling for Product Quality Prediction of Mineral Processing Using Modeling Error PDF Shaping and Entropy Minimization

This paper presents a novel offline modeling for product quality prediction of mineral processing which consists of a number of unit processes in series. The prediction of the product quality of the whole mineral process (i.e., the mixed concentrate grade) plays an important role and the establishment of its predictive model is a key issue for the plantwide optimization. For this purpose, a hybrid modeling approach of the mixed concentrate grade prediction is proposed, which consists of a linear model and a nonlinear model. The least-squares support vector machine is adopted to establish the nonlinear model. The inputs of the predictive model are the performance indices of each unit process, while the output is the mixed concentrate grade. In this paper, the model parameter selection is transformed into the shape control of the probability density function (PDF) of the modeling error. In this context, both the PDF-control-based and minimum-entropy-based model parameter selection approaches are proposed. Indeed, this is the first time that the PDF shape control idea is used to deal with system modeling, where the key idea is to turn model parameters so that either the modeling error PDF is controlled to follow a target PDF or the modeling error entropy is minimized. The experimental results using the real plant data and the comparison of the two approaches are discussed. The results show the effectiveness of the proposed approaches.

Multi-objective Hybrid Intelligent Optimization of Operational Indices for Industrial Processes and Application

AbstractTo pursuit the plant-wide optimization of multiple units industrial process, a hybrid intelligent optimization approach under dynamic environment is proposed. The objective of optimization is that the production indices defined as the performance related to the final product quality, yield, energy and material consumption fall into their target ranges; whilst the decision variables are operational indices of each unit, which is related to units’ intermediate product quality, efficiency and consumption. In this context, the domain knowledge of process engineers are mimicked and combined with the framework in terms of feedback, prediction and feed-forward schemes so as to realize the required optimization. The effectiveness of the proposed approach has been demonstrated by the practical application results.

Steady-State Simulation and Optimization of an Integrated Gasification Combined Cycle Power Plant with CO2 Capture

Integrated gasification combined cycle (IGCC) plants are a promising technology option for power generation with carbon dioxide (CO2) capture in view of their efficiency and environmental advantages over conventional coal utilization technologies. This paper presents a three-phase, top-down, optimization-based approach for designing an IGCC plant with precombustion CO2 capture in a process simulator environment. In the first design phase, important global design decisions are made on the basis of plant-wide optimization studies with the aim of increasing IGCC thermal efficiency and thereby making better use of coal resources and reducing CO2 emissions. For the design of an IGCC plant with 90% CO2 capture, the optimal combination of the extent of carbon monoxide (CO) conversion in the water−gas shift (WGS) reactors and the extent of CO2 capture in the SELEXOL process, using dimethylether of polyethylene glycol as the solvent, is determined in the first phase. In the second design phase, the impact of local...

Gray-Box Model Identification of an Integrated Plant with Recycle

The objective of this paper is to propose a systematic method for identifying a dynamic model of an integrated plant with recycle for plantwide optimization and control purposes. We are interested ...

A Hybrid Programming Model for Optimal Production Planning under Demand Uncertainty in Refinery

Production planning under uncertainty is considered as one of the most important problems in plant-wide optimization. In this article, first, a stochastic programming model with uniform distribution assumption is developed for refinery production planning under demand uncertainty, and then a hybrid programming model incorporating the linear programming model with the stochastic programming one by a weight factor is proposed. Subsequently, piecewise linear approximation functions are derived and applied to solve the hybrid programming model under uniform distribution assumption. Case studies show that the linear approximation algorithm is effective to solve the hybrid programming model, along with an error ≤0.5% when the deviation/mean ≤20%. The simulation results indicate that the hybrid programming model with an appropriate weight factor (0.1-0.2) can effectively improve the optimal operational strategies under demand uncertainty, achieving higher profit than the linear programming model and the stochastic programming one with about 1.5% and 0.4% enhancement, respectively.

An introduction to a dynamic plant-wide optimization strategy for an integrated plant

In this paper, we develop a dynamic real-time optimization (RTO) strategy for an integrated plant. The steady-state assumption in conventional RTOs severely limits frequency of optimization and precludes the use of dynamic degrees of freedom available in the plant (e.g. storage capacities), resulting in suboptimal economic performances. Other approaches that attempt to synchronize the frequency of the plant-level optimization to that of the local-unit-level model predictive controllers can be sensitive to local disturbances and model uncertainty in high-frequency parts of plant dynamics. We reason that a logical middle ground is to perform a dynamic optimization but at a rate significantly lower than the model predictive controllers in order to keep the modeling and computational requirements at a reasonable level for the optimization. We discuss the obtaining of a reduced-order model for a chosen optimization frequency and the interfacing of the real-time optimizer with unit controllers. Two examples are given to compare the various approaches.

Dantzig-Wolfe Decomposition and Large-Scale Constrained MPC Problems

Model Predictive Control (MPC) strategies are typically implemented in two levels: a steady-state target calculation and a control calculation. The steady-state target calculation consumes excess degrees of freedom within the control problem to provide optimal steady-state performance with respect to some specified objective. In some MPC approaches, the target calculation is formulated as a Linear Program (LP) with a pre-specified objective function and a linear or Iinearized steady-state model derived from that used in the control calculation. In large-scale problems, centralized MPC schemes find the optimal solution for the plant-wide optimization problem, but may not provide sufficient redundancy or reliability and can require substantial computation. On the other hand, in a decentralized MPC scheme, the target calculations are performed independently by ignoring interactions among units, and as a result will not usually find the optimal operation. In contrast to the centralized MPC approach, a decentralized MPC provides a high degree of redundancy with respect to the failure of an individual MPC. For largescale process control problems, the desired characteristics for an MPC implementation include: accurate and quick tracking of the changing optimal steady-state operation, a high degree of reliability with respect to failure within the MPC application (i.e., failure of a portion of the control system), and low computational requirements. Fully centralized or monolithic MPC and independent block-wise decentralized MPC represent the two extremes in the “trade-off” among the desired characteristics of an implemented MPC system. In this paper, we propose a coordinated, decentralized approach to the steady-state target calculation problem. Our approach is based on the Dantzig-Wolfe decomposition principle and has been found to be effective at finding the optimal plant operation while providing a high degree of reliability at a reasonable computational load.

MOLECULAR MODELING FOR PLANT-WIDE OPTIMIZATION

In this article, molecular modeling of process streams and processes is combined with overall refinery optimization to select feedstocks and products, optimize the operation of different processes, and determine the most economic modifications. The molecular compositions of the hydrocarbon mixture are described using a MTHS (Molecular Type Homologous Series) matrix. To model reaction processes, structure-reactivity correlations are developed by regressing a wide range of pilot plant and commercial data. Molecular level kinetics is combined with current reactor modeling techniques. To integrate molecular modeling with plant-wide optimization, the interface between molecular compositions and bulk properties is also developed. The plant-wide optimization consists of a two-level optimization procedure, namely the site level and process level. The site level optimization addresses the overall trade-offs among the selections of feedstocks, products, processes, and the use of utilities. The process level optimization optimizes the individual processes by adjusting the detailed operating parameters. With the detailed molecular information available from the molecular model, it is possible to distinguish the contributions from different feedstocks and optimize the operation conditions to produce the most desirable products. A feedback mechanism is built for the coordination between these two levels. By optimizing the overall plant using molecular models, overall profit will be maximized by maximizing desired molecules to form valuable products, minimizing undesired ones, and routing all the molecules to their most appropriate destinations.

MOLECULAR MODELING AND OPTIMIZATION FOR CATALYTIC REFORMING

In this paper, molecular modeling and optimization for the naphtha catalytic reforming process is studied. The catalytic reforming process is for producing high octane number gasoline by reforming reactions in three sequencing fixed bed reactors. Feed naphtha coming from an atmospheric distillation unit consisted of molecules from C5 to C10 including paraffin, iso-paraffin, naphthene, and aromatic. The molecular reaction network consisted of paraffin cracking, naphthene side-chain cracking, aromatic side-chain cracking, ring opening, ring closure, paraffin isomerization, dehydrogenation, and hydrogenation. A molecular model for catalytic reforming was built. On the basis of the simulation model, a process optimization was performed for feed temperature and pressure under constraints such as benzene content, aromatic content, and RON (Research Octane Number) limitations. High RON was contrasted to low benzene and aromatic content requirements. By optimizing and controlling the reaction pathway, we can obtain a final product with the highest profit and appropriate benzene and aromatic contents and RON value. This example shows significant benefits from applying molecular modeling to optimization in the process level. Since gasoline production is related to many different processes such as reforming, FCC, isomerization, alkylation, and so on, more benefits can be obtained by applying molecular modeling to plant-wide optimization.

Benchmarking combined biological phosphorus and nitrogen removal wastewater treatment processes

This paper describes the implementation of a simulation benchmark for studying the influence of control strategy implementations on combined nitrogen and phosphorus removal processes in a biological wastewater treatment plant. The presented simulation benchmark plant and its performance criteria are to a large extent based on the already existing nitrogen removal simulation benchmark. The paper illustrates and motivates the selection of the treatment plant lay-out, the selection of the biological process model, the development of realistic influent disturbance scenarios for dry, rain and storm weather conditions respectively, the definition of performance indexes that include the phosphorus removal processes, and the selection of a suitable operating point for the plant. Two control loops were implemented: one for dissolved oxygen control using the oxygen transfer coefficient K La as manipulated variable, the second one for nitrate control in the anoxic zone using the internal recirculation flow rate as manipulated variable. Dynamic simulations for different dissolved oxygen set points illustrate the complex interactions in this plant, and the necessity for a continuous trade off between supplying sufficient oxygen to promote nitrification on the one hand, and the need for low dissolved oxygen concentrations on the other hand to allow sufficient development of phosphorus accumulating organisms. The potential for aeration energy savings in the plant is highlighted based on the dissolved oxygen profiles resulting from open loop simulations with a dynamic dry weather influent scenario. The influence of the dissolved oxygen set point selection on the nitrate control loop performance observed in the simulations further illustrates the need for a plant-wide optimization approach to reach optimal plant performance.

Challenging control problems and emerging technologies in enterprise optimization

As the scale of control systems increases from individual model predictive control (MPC) applications to integrated control systems for enterprise optimization, two challenges arise. First, the classic MPC regulatory control formulation is not always sufficient—the multivariable controller needs to be more than a regulator. Second, dynamic coordination among MPC controllers is a key to tight integration between advanced process control and plantwide optimization. The first half of this paper discusses the common control needs in the process industries and proposes a range control solution. The balance of the paper discusses the needs and complexity of the dynamic coordination problem, and proposes a three-tier integration method. Because enterprise optimization is a relatively new endeavor, this paper focuses on problems and issues, as well as solutions. The problems and proposed solutions are intended to stimulate discussion and attract more research interest.

Challenging control problems and emerging technologies in enterprise optimization

As the scale of control systems increases from individual model predictive control (MPC) applications to integrated control systems for enterprise optimization, two challenges arise. First, the classic MPC regulatory control formulation is not always sufficientthe multi variable controller needs to be more than a regulator. Second, dynamic coordination among MPC controllers is a key to tight integration between advanced process control and plantwide optimization. The first half of this paper discusses the common control needs in the process industries and proposes a range control solution. The balance of the paper discusses the needs and complexity of the dynamic coordination problem, and proposes a three-tier integration method. Because enterprise optimization is a relatively new endeavor, this paper focuses on problems and issues, as well as solutions. The problems and proposed solutions are intended to stimulate discussion and attract more research interest.

Pricing interprocess streams using slack auctions

AbstractWhen plantwide optimization problems must be decomposed and solved in pieces due to numerical complexity or practical concerns, it is natural to view each plant division as a separate profit maximizer. For a division to maximize its profit, however, it must know the prices of interprocess streams. It is claimed that the correct way of finding these prices is to define slack resources over the process streams and price them using slack auctions. When slack auctions terminate properly, it can be shown that the plant divisions' solutions are globally optimal. How slack auctions are related to Lagrangean relaxation and how they can overcome some of the problems that standard decomposition and relaxation techniques have are discussed.