Control Rehabilitation Impact on Production Efficiency of Ammonia

Article pubs.acs.org/IECR

Control Rehabilitation Impact on Production Efficiency of Ammonia Synthesis Installation Paweł D. Domański,*,† Sebastian Golonka,‡ Robert Jankowski,¶ Paweł Kalbarczyk,¶ and Bartosz Moszowski‡ †

Institute of Control and Computational Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland ‡ Grupa Azoty, Zakłady Azotowe Kędzierzyn S.A., ul. Mostowa 30 A, skr. poczt. 163, 47-220 Kędzierzyn−Koźle, Poland ¶ Valmet Automation Sp. z o.o., ul. Kościuszki 1c, 44-100 Gliwice, Poland ABSTRACT: This article presents final technical and economical results of the comprehensive control rehabilitation project done for the ammonia synthesis plant at JP Nawozy, GA ZAK S.A. The project included the following activities: review of the existing control philosophies and instrumentation infrastructure, design and implementation of the new control loop templates, and tuning of the revised controls over the entire installation. These activities improved installation operation with much more reliable and repeatable dynamic responses. Simultaneously, the overall installation efficiency has been also increased. This initiative is considered as the prerequisite for further control system upgrade with use of advanced process control and process optimization.

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INTRODUCTION Control system quality plays crucial role of the operation performance of any process production installation. The chemical industry is not an exception. This subject may be addressed from different perspectives. We can determine three main related aspects that should be taken into consideration: (1) control performance assessment, i.e., analysis and evaluation of control quality using appropriate methodologies and measures,1 (2) predicting improvement potential and economic benefits associated with control system improvements; although the subject is complex and there is no common agreement how that subject should be assessed, there are several approaches,2 (3) improvements in control technologies, resulting in the introduction of modern algorithms into process industry. There is wide scope of different predictive and adaptive strategies recently,3 often using soft computing methods.4 They are often named with single notion of advanced process control (APC). They are frequently substituted with an upper level addressing longer-term economic efficiency called process optimization (PO).5 The presented paper includes elements of each of the abovementioned areas, which are integrated into a comprehensive methodology. Growing awareness that installation effectiveness might be increased with better controls constitutes the cause for improvement initiatives. Such a consciousness is a starting point for further activities. The next step is to perform a site study assessing current process and control system status. © XXXX American Chemical Society

The study should comprehensively cover all impacts, i.e., process performance, control system infrastructure (distributed control system (DCS), programmable logic controllers (PLC), or supervisory control and data acquisition (SCADA)), instrumentation equipment (actuators and sensors), and control philosophy, together with regulation dynamic performance and controller tuning. The assessment should not only measure control system performance, but also should evaluate indicators for loop dynamic quality, and relationships with overall installation economic key performance indicators (KPIs). The current performance baseline must be evaluated. Finally, improvement potential should be anticipated, together with an implementation roadmap. One should be aware that such a study may end up with negative conclusions, identifying that improvement is neither achievable nor economically feasible. Any control system improvements should start with refurbishment or upgrades in control system and accompanied instrumentation. Furthermore, one may start to work on base control structures and system tuning. The level of base regulation is responsible for proper dynamic responses and realization of set point changes. Once it works properly, upper level changes in APC or PO can be implemented. Process performance indicators should be measured and validated after each step to constantly assess economic feasibility. Received: July 29, 2016 Revised: August 30, 2016 Accepted: September 6, 2016

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DOI: 10.1021/acs.iecr.6b02907 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

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Industrial & Engineering Chemistry Research

Figure 1. Graphical representation of the same limit rule.

• statistical factors utilizing different probabilistic distribution function (standard deviation, variance, skewness, kurtosis, scale or shape, ...); • minimum variance and model-based measures; • novel indexes using wavelets,12 Fourier transform,13 orthonormal functions,7 singular spectrum analysis,14 neural networks,15 Hurst exponent,16 persistence measures,17 entropy,18 ...; and • business KPIs expressed in monetary terms. Statistical factors using Gaussian and non-Gaussian distributions accompanied by persistence measures are used in the present case. Predicting the Economic Benefits of Control Improvements. The task to predict possible improvements associated with an upgrade of a control system has existed in the literature for a long time.10 From the early days, it was mostly associated with the implementation of advanced control. There are three well-established approaches, called same limit, same percentage, and f inal percentage rules.19 All of them are based on the evaluation of the normal distribution for selected variable keeping information about economic benefits and its modifications. Thus, the method assumes Gaussian properties of the process behavior. Improvement potential is evaluated based on the wellestablished algorithm presented below:20 (1) Evaluate histogram of the selected variable. (2) Fit normal distribution to the obtained histogram, which is described by two parameters: the mean value and the standard deviation (σ). (3) It is assumed that the mean value (Mimprov, Mnow) is kept within some selected margin from potential upper (or lower limitation). For the 95% confidence level, it is equal to α = 1.65. Such a value is used in the calculations. The mean value for the improved operation is estimated using the equation

Further possible initiatives might be associated with APC implementation and consecutive economic optimization.6 This paper follows a presented good practice implementation path. It starts with a description of the applied tools and methods. This paragraph concludes with the best practice methodology definition. It is followed by a description of the process, which is ammonia synthesis installation. Results are summarized, commented, and concluded with a discussion of additional opportunities.

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APPLIED METHODS AND ALGORITHMS The presented methodology uses a wide scope of methods associated with tasks of control performance assessment, prediction of the benefits associated with better control, and regulation algorithms and structures. Control Performance Assessment. We observe ongoing research on the evaluation of various approaches and measures supporting the task of process performance assessment due to the regulation behavior. Control systems often perform inefficiently, because of several internal and external reasons.7 We may enumerate the most important ones, i.e., insufficient daily control maintenance, process fluctuations, instrumentation malfunctioning, inappropriate control logic, poor tuning, varying operating conditions, shortage of experienced personnel, etc. Human maintenance is often insufficient and does not cover all needs. Researchers and engineers continuously work on the development of autonomous supporting tools that would evaluate clear measures reflecting the situation and translating technical numbers into economic ones. This process ends up with the design of commercially available software solutions. The first report in this area was presented by Astrom8 in 1967 for a pulp and paper plant. Research continued in the 1970s9 and 1980s.10 In 1989, the Harris minimum variance method11 appeared to gain significantly increasing interest and applicability. Currently, it covers almost all aspects of control that are encountered in practice. Within such a long history and wide research scope, different domain groups of methods were evaluated: • time domain indexes based on step response: undershoot, overshoot, response times, area index, output index, Rindex, idle index, etc.; • time-series-based indexes: mean square error (MSE), integral of absolute error (IAE), amplitude index (AMP);

M improv = M now α(σ1 − σ2)

(1)

This is depicted in Figure 1. (4) Finally, improvement is calculated using the following equation: ΔM (%) = 100 × B

M improv − M now M improv

(2) DOI: 10.1021/acs.iecr.6b02907 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

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Despite its popularity, APC did not have as wide an impact on control technology development in the process industries as it could have been expected.29 Nonstationary behavior of the process results in its parameters varying with time are the main reason. This feature always happens; however, its extent may be significant or negligible. In the case of chemical processes, it is an important factor and cannot be forgotten. Nonstationarity is often coupled with process/technology nonlinearities, which, in the chemical industry, play a very important role. Since the process is nonlinear, simple linear single-input single-output (SISO) loops are not relevant and more advanced tools are needed. Because of the process instrumentation and operation, safety constraints of different origins (i.e., process, procedural, human-originated) also must be taken into consideration. One should remember that insight into the process may be fragmentary, which causes uncertainties. On the other hand, a fast-changing economy requires constant changes in product throughput, features, and quality. It results in varying performance criteria and a continuous need to manipulate the operating points. Unfortunately, the growing economy creates an insatiable need for highly educated and experienced professionals. The lack of on-site continuous system supervision and daily maintenance also starts to become an important factor. A comprehensive summary of different APC solutions in industrial practice is presented in ref 30, while detailed analysis of existing applications, their benefits, and shortcuts is presented in ref 29. In conclusion, APC project success premises are as follows: (1) Professional co-operation between project team members allows for knowledge transfer. (2) Solid-proof feasibility analysis shows both opportunities and bottlenecks. (3) Instrumentation (actuators and sensors) assessment and validation minimizes hardware risks. (4) Base control fine-tuning satisfies requirements for dynamic responses and disturbance rejection. (5) Clear commissioning procedures constitute solid project framework. (6) Risk management plan with balance between fixed prices versus profit-based schemes minimizes risk exposure. (7) Maintenance program allows for results sustainability. (8) Training keeps plant personnel aware of the technologies that are used. Methodology for Plant Improvement with Controls. These previously defined tools are used to implement control system rehabilitation. The goal is to improve installation performance with best practice methodology. Problem formulation, focused on an ultimate target, enables appropriate project harmonization and scheduling allowing selection of optimal tools and methodologies. It has one additional benefit: it does not point out any predefined and assumed technology to be used. The tools are selected according to the targets. It consists of several working stages separated with decision milestones. Each milestone is associated with assessment validating current status, its performance baseline, and improvement potential. We may distinguish five main stages, starting with an initial feasibility study. Stage 0: Initial feasibility study delivers a picture of the installation operating conditions from the perspective of instrumentation and control system. It consists of the evaluation

Although the approach has some deficiencies, it is practically used. An assumption about a Gaussian variable distribution is its main shortcut. Base Control. Base regulatory layer is the core of any process control system. It is implemented within the plant control system: DCS, PLC, or SCADA. Its main goal is to realize appropriate dynamic responses with two contradictory goals: fast and accurate set-point tracking and efficient disturbance rejection. Most often, control philosophy is built around a single-loop PID algorithm. This is called single-element control. In some cases, a two-element structure (cascade) is used, in the case of processes that can be decomposed into fast and slow subprocesses. In addition, such control can be improved with disturbance decoupling realized by feedforward elements (threeelement control). The use of modern linear structures is strongly suggested, to increase control system performance. These technologies enable one to achieve the highest possible results coming from the standard regulating control loops. They include techniques such as gain scheduling (parameter adaptation), set point feedforward for smooth transient periods and protection of control signal constraints violation, Smith predictor enabling dealing with large delay systems, signal filtering with lead-lags, and static linearization. As the result of the implementation of adequate structure and fine-tuning of the base controls of its parameters bring plant control into the best possible status achievable without implementation of advanced control nor optimization. The possible application of any APC tool then is reasonable; the process is controllable and observable from the perspectives of any supervisory APC control and/or optimization. Advanced Process Control (APC) and Process Optimization. APC is extremely capacious notion describing almost anything more complicated than proportional−integral−differential (PID)-based regulatory controls, such as multi-input multi-output (MIMO) control with disturbance decoupling, predictive control (model predictive control, MPC), adaptive control, such as model reference adaptive control (MRAC), selftuning regulators (STR), gain scheduling (GS), robust control, and any structures using sof t computing, i.e., artificial neural networks (ANNs), fuzzy logic techniques, evolutionary computation, biology-driven approaches, .... Practice shows that MPC wins the leading position.3 In fact, the story of advanced control started in early 1960s with the concepts of optimal control that were introduced by Kalman.21 Predictive control (MPC or receding horizon control (RHC)) is based on cyclic search for the optimal control scenario. There are two main classes described by the way the control rule is evaluated.22 In analytical controllers, control rule is calculated once using the Hamilton−Jacobi−Bellman equation (linear models with quadratic performance index). Generally, cyclical solving of the optimization task over receding horizon is performed every sampling period. Such an approach is denoted as nonlinear optimization (NO) (without or with constraints). Main inventions in MPC technology are IDentification and COMmand (IDCOM, 1976),23 Dynamic Matrix Control (DMC, 1979),24 Quadratic Dynamic Matrix Control (QDMC, 1983),25 Model Algorithmic Control (MAC, 1983),26 Generalized Predictive Control (GPC, 1987),27 Rate Optimal Control (ROC, 1991),28 and many others different variations (i.e., nonlinear MPC, stochastic MPC, etc.). C


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• Median: signal median value; • StdDev: standard deviation of the signal, which is defined as

of both control system dynamical performance indexes and baseline economic KPIs. Stage 1: Initiatives to put process instrumentation (actuators and sensors) to the good operating conditions. The phase must be closed by relatively long-term stabilization period (at least a few months), allowing for results settlement. Stage 2: Base regulatory system optimization consisting of design and implementation of proper control philosophy and its fine-tuning. The phase must be closed by relatively long-term stabilization period (at least a few months), allowing for results settlement. Stage 3: Design, implementation, and tuning of supervisory APC algorithms concluded with stabilization period (at least a few months), enabling results settlement. Stage 4: Economic process optimization targeting at financial benefits due to the proper coordination of installed algorithms at all control layers. It should also be summarized using a stabilization period. Bottom−top project formulation allows orchestration of the tasks and monitoring of the achieved milestones. They are described below. Stage 0: Initial Feasibility Study. Initial project planning plays a crucial role in the entire process. Goals are confronted with technology, site instrumentation, control philosophy, DCS and IT infrastructure, available algorithms, economical expectations, and limitations. The analytical part covers four main areas: instrumentation, control philosophy, DCS infrastructure and calculation procedures to evaluate performance indexes and efficiency baselines. Activities consist of the two main phases. First, comprehensive installation review is performed. This analysis has been performed by an expert team of all project stakeholders: the technology owner, the control system provider, and the research organization supporting the parties with scientific expertise. These activities are performed on-site and include collection of the historical data stored in the plant historian database, review of plant documentation, and P&ID (Process & Instrumentation Diagram) drawings and conversations with key site personnel. The team must review and analyze all existing control logics and associated tuning parameters, together with the conditions of site instrumentation (sensors and actuators). In addition, the team holds meetings and conversations with plant personnel, especially members of the operation team (engineers, shift supervisors, operators) to gather their comments on daily operation and common issues. The second part is performed in the office. The collected information is sorted, data are analyzed, and appropriate KPIs are evaluated. KPIs are mostly unitary media consumption indexes. For an ammonia plant, the case is unit consumption of natural gas per ammonia produced. The dynamic quality of the control loops is assessed according to the following best-practice procedure: (1) Review of the loop mode of operation (i.e., AUTO or MAN). (2) Calculation of statistical measures for selected typical period and graphical representation of the time trends for the set point, controlled variable (CV), and manipulated variable (MV). As the main statistical measures, the following indexes were calculated:

N

∑i = 1 (xi − x ̅ )2 (3) N−1 These parameters inform us about the data distribution and variations. • Kurtosis reflects data clustering and density. This index shows if the according data distribution is slender or flat and how it is concentrated around the mean value. The higher the value, the flatter the distribution. StdDev(x) = σ =

Kurtosis(x) =

1 Nσ 4

N

∑ {(xi − x ̅ )4 i=1

− 3} (4)

• Asymmetry is measured by skewness, which provides information about whether the distribution is biased toward values that are higher or smaller than the average: Skewness(x) =

1 Nσ 3

N

∑ (xi − x ̅ )3 i=1

(5)

(3) If the loop works in AUTO mode, the control error is calculated and analyzed. Its histogram is plotted and fitted with curves describing three different probabilistic distribution functions (PDFs): normal Gauss, Cauchy, and α-stable. Following this analysis, one major observation has been done. It is clearly visible that most of the control errors does not hold assumptions that they may be well-approximated using the Gaussian function. This assumption holds only for 7 out of a total of 58 control loops working in AUTO mode (only 12%). This is a very important observation and, thus, the fractal nonlinear time series analysis has been added.31 Thus, as an additional measure, Hurst exponent32 analysis is performed with plotting of R/S plots. Hurst worked on river Nile dams having a history of 847 river overflows. It was assumed that they are dependent on history; Hurst proved the opposite. Hurst expanded the Einstein model of Brownian motion toward a rescaled range R/S. ⎛R⎞ H ⎜ ⎟ = cn ⎝ S ⎠n

(6)

where S is the standard deviation at moment n, c is a positive constant, n represents the number of observations, and H is the Hurst exponent. It is calculated using the expression ⎛R⎞ ln E⎜ ⎟ = ln c + H ln n ⎝ S ⎠n

(7)

plotted in double logarithmic scale. E(R/S)n from n, estimating H as the line slope. Selection of this index is confirmed by ref 33. (4) Characteristics of actuators (i.e., CV vs MV) are calculated to identify eventual nonlinearities or strange clusters of points. (5) If required, additional time trends were collected from the plant historian and sketched. Identified issues and problems enable one to formulate recommendations and an implementation roadmap disclosing possible bottlenecks and risks backed with the risk management plan. It closes with the document describing all performed activities, results, synthesis of the proposed rehabilitation schedule, and a risk management plan.

• Min: signal minimum value; • Max: signal maximum value; • Mean, which is the signal arithmetic average value; D


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Industrial & Engineering Chemistry Research Stage 1: Instrumentation Infrastructure Review. Analysis is divided into three areas: actuators and sensors, control loops, and system related issues. The status of all actuators (valves, dampers, pumps, etc.) is verified. Static characteristics are calculated using historical data. Operation and calibration of sensors is evaluated to confirm proper performance of measurement units. Control system related issues, such as operators, screen panels, and information visualization, data archivization, trending, and alarming system are carefully checked. Finally, all observations are confronted with plant maintenance teams. Results are delivered to the plant owner, which helps to make reasonable and optimal investment decisions and allows one to remove instrumentation bottlenecks. Stage 2: Base Regulating Control Upgrade. Graphical representation of the base regulating control upgrade stage is presented in Figure 2.

Figure 3. Diagram of advanced process control (APC) and process optimization (PO) implementation.

level. Tuning must be performed in two phases (main and finetuning) separated with a stabilization period.

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DESCRIPTION OF THE AMMONIA PRODUCTION PLANT Hydrogen is indispensable for the production of ammonia. In the ZAK ammonia installation, hydrogen is produced in the autothermal reforming (ATR) process of methane, which is a component of the natural gas with the use of pure oxygen. The preparation of the hydrogen for further ammonia synthesis consists of the following subprocesses: (1) compression of the natural gas and oxygen (external raw materials) (2) heating up of both raw materials in the preheaters, (3) autothermal reforming of natural gas, (4) CO removal from process gas (high-temperature shift and low-temperature shift), (5) CO2 removal from process gas (CO2 absorption in propylene carbonate and then CO2 absorption in potassium carbonate solution), and (6) methanation of CO and CO2 residuals. Hydrogen obtained in the methanation plant goes to the mixer, where it is mixed with nitrogen (external raw materials), and then the mixture is compressed in the piston compressors. Next, the synthesis gas is flowing toward the oil removal, then to the freezing plant, and finally to the ammonia synthesis loop. The reaction occurs in the ammonia synthesis reactor on the ferric catalyst. Figure 4 presents a draft diagram of the ammonia production plant. From the optimization point of view, this process generally may be decomposed to the main three subprocesses: raw material preheating and autothermal reforming, removal of CO and CO2 from the process gas, and ammonia synthesis. Raw Material Preheating and Autothermal Reforming. The required natural gas heating temperature of 530 °C is obtained in the natural gas heater. It is a vertical apparatus consisting of three heat exchange sections, where natural gas gets heat from the hot flue gases resulting from combustion of the heating gas in the bottom radiation heater element. In this heater, steam is added to the natural gas.

Figure 2. Diagram of base control rehabilitation.

The main tasks performed are described as follows: (1) Preparation of new template for SISO control loops for single and cascade structures. This template incorporates functionalities, such as filtering, linearization, disturbance decoupling, and gain scheduling of tuning parameters, according to the operating point. (2) DCS system upgrade. (3) Uploading of new logics during planned installation shutdown. (4) Realization of the multistage tuning for all control loops organized in the step-by-step procedure. (5) Results analysis, documentation preparation, and operators training. (6) Re-evaluation of the needs for further control upgrades, especially using supervisory advanced controls and optimization. Stages 3 and 4: Advanced Process Control (APC) and Process Optimization (PO). A typical graphical representation of APC and optimization implementation is presented in Figure 3. Implementation of the APC controls and PO is quite similar to the base control phase; however, different algorithms are used. There are two main issues that should be taken into consideration. Design and implementation of proper hookups enables bumpless switching on and off of the APC supervisory E


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Figure 4. Draft diagram of the ammonia production installation.

The second raw material required for the autothermal reforming, oxygen, is heated in the oxygen heater up to a temperature of ∼440 °C. Its construction is similar to the natural gas heater, with the only difference being that it consists of two heat exchanger sections. Steam is added to oxygen in this heater. Both heaters are heated with burned coke gas or expansion gas (hydrogen-rich gas from the expansion of liquid ammonia). In case of a shortage of coke gas, they may be heated with burned natural gas. The higher the heating temperature, the better the indexes of the consumption of natural gas and oxygen. A sufficient heating temperature of the materials is also necessary for the initialization of the partial oxidation in the space above catalyst. The process occurs in the autothermal reformer on the nickel catalyst. Gas−steam and oxygen−steam mixtures after preheating with temperatures of 530 and 440 °C, respectively, are conducted to the ATR burner. At the burner outlet, the intense mixing of fast flowing reagents occurs. As a result, part of the methane partially oxidizes, supplying the heat that is required for endothermic reactions of methane reforming with steam in the catalyst layer. Because of the fact that the lowest methane content in the resulting process gas is demanded, there is a need to use, with increased reaction pressure, both high steam excess and high temperature. The reaction temperature increase is limited with durability of the catalyst and equipment construction materials.

An excessive steam ratio causes increased raw material consumption. Removal of CO and CO2 from the Process Gas. The water gas shift (WSR) reaction is used to decrease the CO content in process gas from 20 vol % to 0.25 vol %, with a simultaneous increase in hydrogen content. The conversion process occurs in the high-temperature shift (HTS) reactor on the Fe−Cr catalyst and in the low-temperature shift (LTS) reactor on the Cu−Zn− Al catalyst. The use of propylene carbonate to remove CO2 from the process gas has the following advantages: (i) low vapor pressure and (ii) high CO2 solubility, with an insignificant amount of other components of the synthesis gas. After absorption of the propylene carbonate, the process gas still consists of ∼6−8 vol % CO2 and requires further cleaning. CO2 absorption in a Benfield solution occurs in a Model F-105 absorber filled with Pall rings. Additional activator is used to increase the reaction speed. The methanation process of the CO and CO2 residuals in process gas occurs on the nickel catalyst. Ammonia Synthesis. Ammonia synthesis is a balanced process, where gases leaving the reactor consist of ∼15 vol % ammonia. The process is realized in a so-called “synthesis loop”. The flow of the gases in the loop is forced by the centrifugal compressor driven by a steam turbine (under a steam pressure of 4.0 MPa). The ammonia synthesis reaction occurs on the ferric catalyst, while the produced heat is used to produce a steam F


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Figure 5. Typical valve characteristics.

Figure 6. Exemplary control error histogram and probabilistic distribution function (PDF) fitting.

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pressure of 4.0 MPa and preheating of the gas entering the reaction. The process, depending on the installation load, operates at a pressure of 20−30 MPa. The temperature on the catalytic bed is ∼500 °C. After the reaction, ammonia is condensed through shell-and-tube heat exchangers, known as “ammonia coolers”. Liquefied ammonia is separated in the separators, decompressed to a pressure of 1.6 MPa, and sent to the pressure tanks (liquid ammonia distribution plant).

RESULTS OF AMMONIA PLANT BASE CONTROL REHABILITATION

Activities and results associated with the regulating base control rehabilitation are described in this section (i.e., existing control system assessment and control logics modernization). Initial Feasibility Study. Actuators analysis uses different information sources: conversation with operators, results of the evaluation of their characteristics based on historical data, and, G


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Figure 7. Exemplary Hurst exponent R/S plot.

Figure 8. Exemplary Hurst exponent plot having a long memory effect.

finally, indications obtained from the plant maintenance team. Exemplary actuator characteristics are sketched in Figure 5. In conclusion, the list of equipment that requires detailed attention during the planned outage is defined. All control loops are analyzed according to a common scheme. Standard, Gaussian, statistical analysis does not include new

information. On the other hand, nonlinear time series characteristics appeared to be very interesting and beneficial. One of the histograms is presented in Figure 6, as an example. The associated exemplary R/S plot with Hurst exponent evaluation is presented in Figure 7. It was also noticed that some of the Hurst exponent plots have double slopes. Slope switch, H


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Industrial & Engineering Chemistry Research which is also called “crossover”, shows a long memory effect (the time when the process loses its memory). An example of such behavior is sketched in Figure 8. The Hurst exponent indicates whether predictability is present in the variable. It also provides information about whether the control error signal is of normal distribution (H = 0.5), antipersistent (ergodic), biased toward average (0 ≤ H < 0.5), or persistent, i.e., changing the magnifying trend (0.5 < H ≤ 1). Its persistence suggests a lot of internal and external disturbances that affect the loop of different origins, varying in time and possibly of human nature, such as operator’s interventions and manual (MAN) mode operation. The double slope feature may suggest what is known as the “loop long memory”, i.e., a longterm period of disturbances probably cross-correlated with feedbacks through process time delays. The authors in ref 33 have observed that the optimal value for the Hurst exponent is 0.5, while smaller values denote aggressive control and bigger value represent sluggish control. These features are independent of disturbances. System-related issues consists of the analysis of operator screens (mimics) and their updates, a review of the operator alarm systems, an upgrade of the plant DCS system (MetsoDNA), and consideration of the comments from the facility personnel (technology, operation, control, and maintenance teams). There are three main economical indexes that are taken into consideration: (1) natural gas and coke gas consumption per hydrogen [× 10−3 Nm3/m3], (2) hydrogen to ammonia consumption index [× 103 m3/ Mg], and (3) natural gas and coke gas consumption, relative to the amount of ammonia produced [Nm3/Mg]. Task of the Base Control Optimization. This was the main implementation task performed during the project. We may enumerate the following activities: (1) Preparation of a new control loop template for both single loop and cascade realization. Structure includes new functionalities, i.e., filtering, linearization of the actuator characteristics, disturbance decoupling, feedforward and gain scheduling for the controller parameters. (2) Upgrade of the MetsoDNA DCS. (3) Uploading of the new controls during the planned shutdown at the installation. (4) Multistage tuning process of the new structures considering dynamic conditions, installation interactions, production economics, and facility safety. (5) Analysis of the project results. Benefits in Dynamic Operation. The control loops summary shows that the share of control loops operating in AUTO mode for >80% of the time increased from 64 to 66. Table 1 shows incorporation of the AUTO mode for all 153 control loops before tuning. Table 2 shows similar indexes calculated after tuning activities. It is also interesting to see a tabular comparison of the basic statistics for the 153 control loops in the main installation. In Table 3, a comparison between the main control loops is presented. Improvement is clearly visible, as more than 50% of the control loops tracks their set point much closer, simultaneously decreasing the variability (measured as the standard deviation of estimated normal distribution) of the control errors.

Table 1. Summary of the AUTO Mode Utilization before Tuning time in AUTO

number of loops

percentage

100% 100% ... 80% 80% ... 20% 20% ... 0% 0%

45 19 2 0 83

21% 9% 1% 0% 38%

Table 2. Summary of the AUTO Mode Utilization after Tuning time in AUTO

number of loops

percentage

100% 100% ... 80% 80% ... 20% 20% ... 0% 0%

49 17 4 0 78

23% 8% 2% 0% 37%

Table 3. Comparison of the Control Loops Dynamic Performance and Set Point Tracking key performance indicator, KPI

“before”

“after”

benefit

average error average error variance average standard deviation loops improved in error absolute value loops improved in error standard deviation average control error absolute value average control error absolute variance

0.75 123.23 8.47

0.41 117.26 8.04 40 44 3.15 106.35

−45% −5% −5% 54% 59% −12% −6%

3.59 112.64

Improvement is also visible with the Hurst exponent measure, as 50% of the control loops improved H (i.e., the value of H is closer to 0.5). Economic Benefits. The economical impact of the of base control optimization (structure modification and tuning) is shown in Tables 4−6. The index values are calculated using reference data collected from time periods before the project. The next column shows predicted improvements evaluated on the basis of the same data using the same limit algorithm. Finally, indexes are calculated on the reference data gathered after the project has been accomplished. Reference data after project completion uses 24-h averages from 54 days of normal operation between September 20, 2014 and November 18, 2014. Reference data before modernization are collected from the time period between November 3, 2013 and May 23, 2014. To avoid statistical errors for the “before” periods, all possible continuous time periods of 54 days were selected. Next, the period with the best indexes were selected to use the most conservative reference and associated improvement. In addition, the maximum and average improvement was also calculated and is shown in the tables. Table 4 presents obtained benefits in the natural gas consumption index, while the next table (Table 5) shows the Table 4. Improvement in the Natural Gas Consumption Index ”before” KPI min mean max I

786.8 792.32 796.85

”after” KPI

change

benefit

771.84

14.96 20.48 25.01

1.90% 2.58% 3.14%


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they are rather expected with the implementation of APC or PO. This project reveals that control system dynamic responses play a much more important role for installation performance than is considered. A well-controlled process enables more stable and safer operation. It gives operators more free space and more degrees of freedom. It allows one to keep the process closer to the optimal operating point. In this project, stabilization of steam methane reforming and its impact on the next process stages are clearly visible. We obtain better CO and CO2 cleaning. Finally, the reactor works more efficiently. The result is a combination of small and minor improvements over the entire installation. In conclusion, APC and PO are not the only remedies for performance deficiencies. There is much to be achieved with dynamic operation of the base regulatory layer. In addition, even the best APC will not help in case of badly tuned dynamic control. Well-established and controlled project execution with clearly defined stages and milestones enables one to solve all identified issues in the bottom−top strategy. We do all the activities that are needed and only them. It should be also noted that the entire project was conducted in close cooperation with the ZAK personnel responsible for ammonia installation (i.e., technology, operation, control, and maintenance teams). The benefits obtained result from the joint contribution and expertise exchange between all of the project team members.

results for joined natural and coke gas consumption index. Finally, the additional index of oxygen consumption ratio is also presented (Table 6). Table 5. Improvement in the Joined Natural and Coke Gas Consumption Index “before” KPI min mean max

818.79 823.61 828.34

“after” KPI

change

benefit

804.54

14.25 19.07 23.80

1.74% 2.31% 2.87%

Table 6. Benefits in Oxygen Consumption Index min mean max

“before” KPI

“after” KPI

change

benefit

236.81 239.81 241.48

229.73

7.08 10.08 11.75

2.99% 4.20% 4.86%

It is worthwhile to address not only quantitative results of the project. The project has improved installation controllability and safety, making the work easier for the operators. The results could be even better; however, the condition of some of the actuators (control valves) forms a bottleneck for further improvement. In addition, it was observed that there still exists potential for further improvement through application of multivariable predictive controllers (MIMO APC). It should be finally noted that, during the installation shutdown, site maintenance teams reviewed and serviced some of the actuators. It is certain that this job contributes to the overall results as well. However, it is not possible to decompose this impact with any quantitative measure. The improvement ratio of the base economic index described as natural gas consumption per ammonia production was also slightly higher than predicted. During the initial feasibility study phase, the improvement ratios were calculated. The “before” value was 786.8, while the predicted value was 776.7 and the result finally obtained is 771.8. Concluding all of the above discussion, one might say that the results not only confirmed project benefits but also proved the entire methodology and initial project assumptions. High project benefits show that there exists a safe and reliable estimate of the project benefits of at least 1% in joined consumption of the natural and coke gas per ammonia unit index, with an additional decrease in oxygen consumption of 3%.

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AUTHOR INFORMATION

Corresponding Author

*Tel.: +48 22 234 7665. Fax: +48 22 825 3719. E-mail: p. [email protected]. Notes

The authors declare no competing financial interest.

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REFERENCES

(1) Jelali, M. An overview of control performance assessment technology and industrial applications. Control Eng. Pract. 2006, 14, 441. (2) Brisk, M. Process control: Potential benefits and wasted opportunities. In Proceedings of the 5th Asian Control Conference, 2004; Vol. 1, pp 10−16. (3) Qin, S. J.; Badgwell, T. A. A survey of industrial model predictive control technology. Control Eng. Practice 2003, 11, 733−764. (4) Cartwright, H.; Sztandera, L. M. Soft Computing Approaches in Chemistry. In Studies in Fuzziness and Soft Computing; Springer: Berlin, Heidelberg, 2012. (5) Edgar, T.; Himmelblau, D.; Lasdon, L. Optimization of Chemical Processes; McGraw−Hill: New York, 2001. (6) McCarthy, E. Plant-wide control optimisation. Eur. Process Eng. 2013, (Feb. 1). Available via the Internet at: http://www.engineerlive. com/content/23775. (7) Jelali, M. Control Performance Management in Industrial Automation: Assessment, Diagnosis and Improvement of Control Loop Performance; Springer−Verlag: London, New York, 2013. (8) Astrom, K. J. Computer control of a paper machineAn application of linear stochastic control theory. IBM J. Res. Dev. 1967, 11, 389−405. (9) DeVries, W.; Wu, S. Evaluation of process control effectiveness and diagnosis of variation in paper basis weight via multivariate time-series analysis. IEEE Trans. Autom. Control 1978, 23, 702−708. (10) Tolfo, F. A. Methodology to Assess the Economic Returns of Advanced Control Projects. In American Control Conference, 1983; pp 1141−1146. (11) Harris, T. Assessment of closed loop performance. Can. J. Chem. Eng. 1989, 67, 856−861.

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CONCLUSIONS AND FURTHER RESEARCH The base control rehabilitation project has met the initially assumed goals. Dynamic responses of the ammonia installation are improved. Safe operation in AUTO mode has been achieved with minimizing periods of operators’ manual interventions (MAN mode). Improved process controllability and repeatable dynamic operation enables conduction of the process with set point values closer to the ones of higher economic performance. All of these features have constructed a solid background for day-to-day operation and allow one to safely prepare for further improvements through application of the advanced process control (APC) and process optimization (PO). Observation and analysis of the project outcome shows one very interesting result. Dynamical benefits associated with base regulatory control are obvious. However, simultaneous significant economic improvement was not directly expected. In fact, J


Article

Industrial & Engineering Chemistry Research (12) Nesic, Z.; Dumont, G. A.; Davies, M. S.; Brewster, D. CD Control Diagnostics Using a Wavelet Toolbox. Proceedings of the International CD Symposium ‘97, Finland, June 1997; IMEKO World Congress: pp 120−125. (13) Schlegel, M.; Skarda, R.; Cech, M. Running discrete Fourier transform and its applications in control loop performance assessment. In 2013 International Conference on Process Control (PC); 2013; pp 113− 118. (14) Yuan, H. Process Analysis and Performance Assessment for Sheet Forming Processes. Ph.D. Thesis, Queens University, Kingston, Ontario, Canada, 2015. (15) Zhou, Y. F.; Wan, F. A neural network approach to control performance assessment. Int. J. Intelligent Comput. Cybern. 2008, 1, 617− 633. (16) Pillay, N.; Govender, P. A Data Driven Approach to Performance Assessment of PID Controllers for Setpoint Tracking. Procedia Eng. 2014, 69, 1130−1137. (17) Domański, P. D. Non-Gaussian and persistence measures for control loop quality assessment. Chaos 2016, 26, 043105. (18) Zhang, J.; Jiang, M.; Chen, J. Minimum entropy-based performance assessment of feedback control loops subjected to nonGaussian disturbances. J. Process Control 2014, 24, 1660−1670. (19) Bauer, M.; Craig, I.; Tolsma, E.; de Beer, H. A Profit Index for Assessing the Benefits of Process Control. Ind. Eng. Chem. Res. 2007, 46, 5614−5623. (20) Ali, M. K. Assessing Economic Benefits of Advanced Control. In Process Control in the Chemical Industries; Chemical Engineering Department, King Saud University: Riyadh, Kingdom of Saudi Arabia, 2002; pp 146−159. (21) Kalman, R. E. A new approach to linear filtering and prediction problems. J. Basic Eng. 1960, 82, 35−45. (22) Tatjewski, P. Advanced Control of Industrial Processes, Structures and Algorithms; Springer: London, 2007. (23) Richalet, J.; Rault, A.; Testud, J. L.; Papon, J. Algorithmic control of industrial processes. In Proceedings of the 4th IFAC Symposium on Identification and System Parameter Estimation, USSR, 1976; pp 1119− 1167. (24) Cutler, C. R.; Ramaker, B. L. Dynamic matrix controlA computer control algorithm. Presented at the AIChE National Meeting, Houston, TX, 1979. (25) Cutler, C.; Morshedi, A.; Haydel, J. An industrial perspective on advanced control. Presented at the AIChE National Meeting, Washington, DC, 1983. (26) Larimore, W.; Mahmood, S.; Mehra, R. K. Adaptive Model Algorithmic Control. In IFAC Adaptive Systems in Control and Signal Processing, San Francisco, CA, June 20−22, 1983; pp 283−28810.1016/ B978-0-08-030565-3.50060-5. (27) Clarke, D.; Mohtadi, C.; Tuffs, P. Generalized predictive control Part I. The basic algorithm. Automatica 1987, 23, 137−148. (28) Apostolakis, I. S.; Jordan, D. Optimal Control of Systems Employing Multirate Output Sampling. In Proceedings of the European Control Conference 1991, Grenoble, France, July 2−5, 1991; pp 361− 366. (29) Smuts, J. F.; Hussey, A. Requirements for Successfully Implementing and Sustaining Advanced Control Applications. Presented at the 2011 ISA POWID Symposium, 2011. (30) Bauer, M.; Craig, I. K. J. Process Control 2008, 18, 2−18. (31) Lopes, R.; Betrouni, N. Fractal and multifractal analysis: A review. Med. Image Anal. 2009, 13, 634−649. (32) Hurst, E. Bayesian Autoregressive Time Series Analysis. IEEE Trans. Syst. Sci. Cybern. 1968, 4, 317−324. (33) Srinivasan, B.; Spinner, T.; Rengaswamy, R. Control loop performance assessment using detrended fluctuation analysis (DFA). Automatica 2012, 48, 1359−1363.

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Control Rehabilitation Impact on Production Efficiency of Ammonia

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