Control structure design for complete chemical plants

Enhanced control structure design for an industrial off-gas system: Simple reconfigurations benefit the economy

For control structure design of the industrial off-gas benchmark system, application of the Skogestad's state-of-art design procedure has suggested the scrubber inlet pressure (P _si in the roaster) and one of the fan speeds (N _fan1 or N__fan2 in the furnace) as the self-optimizing controlled variables CVs. In this study, we stress and advocate the gSOC-plus-BAB approach as an enhanced design toolkit for the classical and systematical design procedure. The gSOC (global self-optimizing control) is able to efficiently solve measurement combinations as CVs with improved economic performances, while the BAB (branch and bound) algorithm serves to fast screen promising measurement subsets for large-scale problems. Using the enhanced design for the off-gas system, our new findings are to control the combination of roaster's ID fan outlet pressures, 0.494P _IDfan1+0.506P _IDfan2 (setpoint: -257.04 Pa), and the furnace's fan pressure difference, P _IDfan1- P _IDfan2 (setpoint: 0). Such simple reconfigurations can dramatically reduce the average economic loss by 53.3% for the roaster and even achieve perfect optimal control for the furnace. Both steady state and dynamic evaluations are carried out to validate the reconfigured control structures. © 2017 Elsevier Inc. All rights reserved.

Rate and grade transition control using PI controller based supervisory and regulatory layers: Diacetone alcohol process case study

Product rate and grade transitions are two significant operations undertaken by process plants to operate in uncertain environments. Rate transitions, typically dictated by economic considerations, are often implemented in open loop via a throughput manipulator (TPM). The absence of feedback of product rate to manipulate the TPM results in an offset during rate/grade transitions as well as in presence of feed-side disturbances. In this work, we explore use of a simple PI controller based supervisory control layer that guides the plantwide regulatory layer to achieve product rate targets during grade transition or in the presence of feed grade disturbances. While such a layer cannot incorporate optimal operation and constraint handling explicitly as in model predictive control, it obviates the need for advanced process control elements that medium-to-small scale industries can ill-afford. The PI based supervisory-regulatory control structure is illustrated using simulated case studies on a prototypical process for diacetone alcohol production comprising of a reactor followed by two distillation columns and a recycle.

Collaborative Control Applied to BSM1 for Wastewater Treatment Plants

This paper describes a design procedure for a collaborative control structure in Plant Wide Control (PWC), taking into account the existing controllable parameters as a novelty in the procedure. The collaborative control structure includes two layers, supervisory and regulatory, which are determined according to the dynamics hierarchy obtained by means of the Hankel matrix. The supervisory layer is determined by the main dynamics of the process and the regulatory layer comprises the secondary dynamics and controllable parameters. The methodology proposed is applied to a wastewater treatment plant, particularly to the Benchmark Simulation Model No 1 (BSM1) for the activated sludge process, comparing the results with the use of a Model Predictive Controller in the supervisory layer. For determining controllable parameters in the BSM1 control, a new specific oxygen mass transfer model in the biological reactor has been developed, separating the kLa volumetric mass transfer coefficient into two controllable parameters, kL and a.

Comparing Composition Control Structures for Kaibel Distillation Columns

Although Kaibel distillation columns are superior to conventional distillation sequences owing to smaller equipment investment and operation cost, they display high nonlinearity and this greatly increases the difficulty of achieving their tight control. To overcome this problem, four decentralized composition control structures, i.e., the CSR/QR, CSR/B, CSD/QR, and CSD/B structures, are proposed and compared based on the control of a Kaibel distillation column fractionating a methanol/ethanol/propanol/butanol quaternary mixture. These four composition control structures all include five composition control loops. While the four of them are employed to maintain the purity of the top, upper sidestream, lower sidestream, and bottom products, the remaining one is employed to minimize the energy consumption of the Kaibel distillation column by maintaining the composition of propanol at the first stage of the prefractionator. Dynamic simulation results show the CSR/QR and CSR/B structures can tightly maintain the purity of the controlled products with a small overshoot and short settling time after facing various disturbances in feed conditions, but the CSD/QR and CSD/B structures lead to oscillatory responses (the latter even shows divergent responses under individual disturbances). At the end of the article, some effective guides for developing composition control systems are given.

A Retrofit Hierarchical Architecture for Real-Time Optimization and Control Integration

To achieve the optimal operation of chemical processes in the presence of disturbances and uncertainty, a retrofit hierarchical architecture (HA) integrating real-time optimization (RTO) and control was proposed. The proposed architecture features two main components. The first is a fast extremum-seeking control (ESC) approach using transient measurements that is employed in the upper RTO layer. The fast ESC approach can effectively suppress the impact of plant-model mismatch and steady-state wait time. The second is a global self-optimizing control (SOC) scheme that is introduced to integrate the RTO and control layers. The proposed SOC scheme minimizes the global average loss based on the approximation of necessary conditions of optimality (NCO) over the entire operating region. A least-squares regression technique was adopted to select the controlled variables (CVs) as linear combinations of measurements. The proposed method does not require the second order derivative information, therefore, it is numerically more reliable and robust. An exothermic reaction process is presented to illustrate the effectiveness of the proposed method.

Dynamic Simulations of the Allam Cycle Power Plant Integrated with an Air Separation Unit

The unprecedented rise in carbon dioxide levels due to anthropogenic activities, if left unchecked, can lead to increased global warming. Electricity and heat generation account for around 25% of this greenhouse gas emission. The Allam cycle, a new oxy-fuel power cycle that emits virtually no CO2 and NOx, is inherently integrated with an air separation plant. In this study, Aspen Plus Dynamics was used to model the integrated Allam power plant/air separation unit (ASU) with a high degree of heat and work integration. The steady-state model developed agrees with the model developed by Net Power. Regulatory and advanced PID controllers were implemented for major equipment to meet operation objectives. Controller set point change, power ramp down, and natural gas composition change were studied, and key plant performance indicators were monitored and analyzed. This study shows that the Allam cycle power plant integrated with an ASU is controllable with the proposed control strategy under a tightly integrated configuration.

Topology Effects on Sparse Control of Complex Networks with Laplacian Dynamics

Ease of control of complex networks has been assessed extensively in terms of structural controllability and observability, and minimum control energy criteria. Here we adopt a sparsity-promoting feedback control framework for undirected networks with Laplacian dynamics and distinct topological features. The control objective considered is to minimize the effect of disturbance signals, magnitude of control signals and cost of feedback channels. We show that depending on the cost of feedback channels, different complex network structures become the least expensive option to control. Specifically, increased cost of feedback channels favors organized topological complexity such as modularity and centralization. Thus, although sparse and heterogeneous undirected networks may require larger numbers of actuators and sensors for structural controllability, networks with Laplacian dynamics are shown to be easier to control when accounting for the cost of feedback channels.

Modeling and Simulation of Energy Systems: A Review

Energy is a key driver of the modern economy, therefore modeling and simulation of energy systems has received significant research attention. We review the major developments in this area and propose two ways to categorize the diverse contributions. The first categorization is according to the modeling approach, namely into computational, mathematical, and physical models. With this categorization, we highlight certain novel hybrid approaches that combine aspects of the different groups proposed. The second categorization is according to field namely Process Systems Engineering (PSE) and Energy Economics (EE). We use the following criteria to illustrate the differences: the nature of variables, theoretical underpinnings, level of technological aggregation, spatial and temporal scales, and model purposes. Traditionally, the Process Systems Engineering approach models the technological characteristics of the energy system endogenously. However, the energy system is situated in a broader economic context that includes several stakeholders both within the energy sector and in other economic sectors. Complex relationships and feedback effects exist between these stakeholders, which may have a significant impact on strategic, tactical, and operational decision-making. Leveraging the expertise built in the Energy Economics field on modeling these complexities may be valuable to process systems engineers. With this categorization, we present the interactions between the two fields, and make the case for combining the two approaches. We point out three application areas: (1) optimal design and operation of flexible processes using demand and price forecasts, (2) sustainability analysis and process design using hybrid methods, and (3) accounting for the feedback effects of breakthrough technologies. These three examples highlight the value of combining Process Systems Engineering and Energy Economics models to get a holistic picture of the energy system in a wider economic and policy context.

Analysis of Control Degrees of Freedom in Batch and Cyclic Processes

Recently, we have proposed a new formulation approach for control degree of freedom (CDOF) analysis of process systems. This formula interrelates the CDOF and elements of a process flow diagram (PFD). The formulation is accurate, easily applicable by process engineers, and needs little prior knowledge of the process under study. This paper describes further about this new formulation and its advantages by applying it to batch and cyclic processes. The results demonstrate the correctness of the method in  determining CDOF for all case studies at both steady state and dynamic conditions. The CDOF for “reheat regenerative Rankine” and “vapor absorption refrigeration” cycles have been determined to be 9 and 8, respectively. The steady state CDOF values for the Rankine and refrigeration cycles have been calculated as 8 and 4, respectivley. Also the CDOF for the process of  “heat pump integrated with batch distillation column”  is 4 that verifies the suggested formula of CDOF. The method also gives beneficial insights about the manipulated variables (MVs) in a control system.

Dynamic Optimization of a Subcritical Steam Power Plant Under Time-Varying Power Load

The increasing variability in power plant load in response to a wildly uncertain electricity market and the need to to mitigate CO2 emissions, lead power plant operators to explore advanced options for efficiency optimization. Model-based, system-scale dynamic simulation and optimization are useful tools in this effort and are the subjects of the work presented here. In prior work, a dynamic model validated against steady-state data from a 605 MW subcritical power plant was presented. This power plant model was used as a test-bed for dynamic simulations, in which the coal load was regulated to satisfy a varying power demand. Plant-level control regulated the plant load to match an anticipated trajectory of the power demand. The efficiency of the power plant’s operation at varying loads was optimized through a supervisory control architecture that performs set point optimization on the regulatory controllers. Dynamic optimization problems were formulated to search for optimal time-varying input trajectories that satisfy operability and safety constraints during the transition between plant states. An improvement in time-averaged efficiency of up to 1.8% points was shown to be feasible with corresponding savings in coal consumption of 184.8 tons/day and a carbon footprint decrease of 0.035 kg/kWh.