Plant Process Control Research Articles

Scenario Design for the Evaluation of Human–Machine Interaction in Modular Process Plants

AbstractOperators in modular process plants face new demands due to features like increasing automation and digitalization combined with an architecture allowing for reconfigurations in short time spans. However, operators must be able to master process control tasks at all times. For research purposes on topics like supporting operators in their performance in process control of modular process plants, this article aims at developing scenarios of such plants. Based on three conceptual levels describing the relation of chemical processes and their modularized realization and a neutralization process as use case, four scenarios were developed. Having the common task to increase the throughput, the scenarios differ in terms of complexity and constraints that need to be considered for process control.

Optimal control of mineral processing plants using constrained model predictive static programming

The model predictive static programming (MPSP) technique, which is extended recently to incorporate applicable state and control constraints, operates on the philosophy of nonlinear model predictive control (NMPC). However, it reduces the problem into a lower-dimensional problem of control variables alone, thereby enhancing computational efficiency significantly. Because of this, problems with larger dimensions and/or increased complexity can be solved using MPSP without changing the computational infrastructure. In this paper, the MPSP technique with applicable constraints is applied to two challenging control problems in the mineral processing industry: (i) a single-stage grinding mill circuit model, and (ii) a four-cell flotation circuit model. The results are compared with a conventional nonlinear MPC approach. Comparison studies show that constrained MPSP executes much faster than constrained MPC with similar/improved performance. Therefore, it can be considered a potential optimal control candidate for mineral processing plants.

The State of Critical and Strategic Metals Recovery and the Role of Nuclear Techniques in the Separation Technologies Development: Review

The extraction of useful minerals or geological materials from the Earth’s crust, most typically from various sources, is crucial to a country’s development and progress. Mineral-rich countries use these resources to transform their economies and propel them toward long-term prosperity. There is an urgent need for the world to increase mineral exploration efforts, improve the recycling of important metal-containing resources, and extract them using upgraded hydrometallurgical procedures with high recovery efficiency. This review paper highlights the importance of strategic and critical metals in the economy and the role of nuclear techniques in the analysis, process optimization, and remediation of metals using solvent extraction, adsorption, and chromatographic resins. Radiotracer analysis, X-Ray Fluorescence spectrometry (XRF), Neutron Activation Analysis (NAA), and X-Ray Diffraction (XRD) are appropriate for improving laboratory-based hydrometallurgical processes, with future technical and economic benefits. The development and installation of novel instruments to provide the real-time control of mining and mineral processing plants for improved control have the potential to aid in the recovery of a broad range of metals.

PER4Mance Prototyping environment for research on human-machine interactions for alarm floods management: the case study of a chemical plant process control

Alarm floods are dangerous because the quantity of alarms triggered is too numerous for operators to reliably implement the right corrective action. Process operators of complex systems, such as chemical plants or nuclear power production, are faced with alarm management systems that can be better built in consideration of human capabilities and limitations. Developing human-machine interfaces (HMIs) that better support operators is critical for ensuring the safe and reliable operation of critical systems and processes. The research team has developed an accessible and adaptable prototyping environment dedicated for research on alarm management and human-machine interactions in the process industry. The method used was to build on the Tennessee Eastman Process (TEP) simulator and incorporate Human-Machine design guidelines. The results are an open-sourced prototyping environment that incorporates data from a real chemical plant and integrates true alarm data and thresholds. At the end of this article, we share the Github link to the entire MATLAB, Simulink and App Designer files of PER4Mance: a prototyping environment for research on human-machine interactions for alarm flood management.

Vissim for the Refinery and Plant Process Control Engineer: A Tutorial

Vissim for the Refinery and Plant Process Control Engineer: A Tutorial

“Context-Dependent Cognitive Workload Monitoring using Pupillometry for Control Room Operators to Prevent Overload”

Occupational Application Modernization of process plants has replaced traditional machine control with sophisticated computerized control, with the work required of control room operators changing from predominantly physical to cognitive. As a result, excessive cognitive workload during an abnormal condition is not an uncommon situation. Almost 70% of accidents in process industries are the result of human error, which is largely modulated by high cognitive workload. A context-dependent methodology using pupillometry was adopted in this study for workload assessment. We found that workload has a significant effect on task performance and subsequent success in handling an abnormal condition. Incorporating contextual information along with pupillometry-based measures was useful to explore the dynamics of variations in workload in depth. In addition, the proposed methodology is a more reliable method for a dynamic environment in which the situation evolves with operator interactions with the process. Technical : Background: Operator workload monitoring has become an important aspect of system engineering due to ever increasing cognitive demands in the control room. Higher cognitive workload, beyond capability, is directly associated with human error in plant operations. Even a small increase in workload during peak demand may result in human error, which can endanger the safety of a process plant. Purpose: Subjective methods are of limited use due to the requirement for near real-time evaluation of cognitive workload. Applications such as process plant control room require the operator to reiterate and re-represent the situation based on the contextual cues from environment. Hence, our objective was to develop and test a methodology that incorporates contextual information in workload measurement using pupillometry. Method: Participants (n = 44) performed simulated process control tasks. Pupil size was recorded using an eye-tracking device. Recorded pupil size was analyzed based on a methodology that incorporates contextual information from the human-machine interface. Events that present new information about the current state of the process were categorized as critical events. Pupil size variation was adopted to provide an estimate of cognitive workload between consecutive critical events. Results: Cognitive workload was associated with process behavior and operator actions. An increase in cognitive workload for specific events was observed when participant actions deteriorated process conditions. We also observed that overall task performance was associated with the frequency of certain events and corresponding variations in pupillary behavior. Conclusion: Incorporating contextual information provides more detailed insights into the dynamics of variations in cognitive workload. The mental representation of a current situation may vary depending on expertise level. The proposed methodology assesses such dynamic mental representations in the form of frequency of critical events and corresponding changes in the cognitive workload that ultimately determines the likelihood of an operator successfully completing a task.

An Arithmetic-Trigonometric Optimization Algorithm with Application for Control of Real-Time Pressure Process Plant.

This paper proposes a novel hybrid arithmetic–trigonometric optimization algorithm (ATOA) using different trigonometric functions for complex and continuously evolving real-time problems. The proposed algorithm adopts different trigonometric functions, namely sin, cos, and tan, with the conventional sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) to improve the convergence rate and optimal search area in the exploration and exploitation phases. The proposed algorithm is simulated with 33 distinct optimization test problems consisting of multiple dimensions to showcase the effectiveness of ATOA. Furthermore, the different variants of the ATOA optimization technique are used to obtain the controller parameters for the real-time pressure process plant to investigate its performance. The obtained results have shown a remarkable performance improvement compared with the existing algorithms.

Assessing inter and intra-variation in the characteristics of faecal sludge from Vadgaon Maval, Maharashtra: For better faecal sludge management in India

Faecal Sludge (FS) produced in developing countries has become a significant source of water pollution. The optimal treatment of FS can lead to a sustainable economy. In India, the second phase of the open defecation free (ODF) initiative (energy recovery by providing city- or town-level centralised FS treatment plants) has begun from 2019 to sustain millions of toilets. However, to achieve this goal, India must address significant knowledge gaps such as the type of available on-site sanitation treatments (OSTs), optimal time of emptying the OSTs, and variations in the FS characteristics with age. Thus, the present study analysed FS samples collected from Vadgaon Maval, Maharashtra, India, to understand the inter- and intra-variation in FS characteristics. The pH, electrical conductivity, total solids (TS), specific gravity (SG), organic matter (OM), chemical oxygen demand (COD), and pathogen content of FS samples were observed to be in the range of 7–7.65, 1.19–4.01 mS/cm, 1–123 g/L, 0.88–1.26, 18.4%–100%, 960–6080 mg/L, and 105–109 number/L of FS, respectively. The inter-variation of FS characteristics exhibited that the pH, EC, TS, and SG increased, whereas OM, settling, and drying capacity decreased with an increase in age. Fresh FS (excreta degraded for ≤3 years) was yellowish-brown in colour and exhibited low TS, low SG, and high OM. On the other hand, older FS (excreta degraded for >3 years) was blackish-brown in colour and exhibited high TS, SG, and pathogen content. This observation indicates that FS vacuum pumping may be difficult in the sludge zone after 3 years of excreta degradation, which may encourage the manual scavenging practice. Additionally, the analysis of FS treatment plant (FSTP) process control parameters (settled sludge volume, sludge volume index, and sludge drying capacity) indicated that the treatment time required for older FS is long, which increases the FSTP cost. Furthermore, FS intra-variation in OST exhibited that effluent FS could be directly sent for effluent treatment without any solid–liquid separation process. Thus, the present study can help practitioners in establishing or modifying FS treatment systems for addressing conditions based on the variation in FS characteristics at the town level in India.

8 Years of Experience in Teaching Process Dynamics and Control with Control Station® Software

The paper describes all the steps of a teaching activity dealing with Process Dynamics and Control and focused on the students’ use of the Control Station® simulation software. After a short software description, the paper discusses the methodology developed for coupling theoretical lecturing and practical PC-lab class, the way of involving students and the use of an interactive software environment to present automatic control of illustrative process plants. These latter comprise unit operations and simple equipment from chemical, biochemical, pharmaceutical and food industries as actual examples of abstract systems and mathematical formalisms introduced for studying processes in the context of dynamics and control. Two Project Works, which were developed by students using Control Station® and discussed by them, are presented as examples. The outcome of this 8-year teaching experience is analyzed on the basis of the number of Project Works annually delivered, the auto-evaluation tests, the final exam scores as well as the relevant answers yearly provided by the students through the Course Evaluation Forms. The final statistical results are positive.

Robust internal model controller with increased closed‐loop bandwidth for process control systems

This study proposes a modified internal model control (IMC) structure in which an additional controller is added to the standard IMC structure to improve the closed-loop system bandwidth. The resulting structure is evaluated to satisfy standard IMC objectives of nominal performance and robust stability. The novelty of the work is to design a fractional order (FO) filter in the IMC structure so that gain margin ( A m ) and phase margin ( ϕ m ) specifications could be independently satisfied for a first-order plus time delay (FOPTD) process control plant. The proposed modified IMC (m-IMC) is simulated for three different FOPTD systems to validate and substantiate the theoretical analysis. The proposed m-IMC is compared with different integer order as well as FO controllers. The transient performance are compared in terms of rise time ( T r ), settling time ( T s ), maximum overshoot (% M p ) and the effectiveness is measured in terms of integral square error. To validate the viability of the proposed control scheme, it is implemented on a laboratory DC servo-system.

Systematic Framework toward a Highly Reliable Approach in Metal Accounting

ABSTRACT Metal accounting is becoming an area of growing focus because of the requirement for the resulting data to feed directly into financial reports. It is vital in the fulfillment of best corporate governance practices and to provide assurance on metallurgical processes. The current metal accounting practices deliver a number of important measures, but an essential change in the approach toward data integrated and auditable metal accounting system is an indispensable need. This work aims to present a systematic framework toward a highly reliable method in metal accounting. The proposed approach introduces a novel mining to product metal accounting scheme and conceptualization of an intelligent metallurgical and metal accounting information management system (MMAIMS). An open cast gold mine in South Africa is presented as a case study. Based on the results and discussions that we have presented in this study, it has been shown that the proposed intelligent MMAIMS will result in improved plant process control, transparent financial reporting, and a suitable understanding of interrelationships between different process activities. The intelligent MMAIMS approach will eradicate data security risks related to multiple-user environment spreadsheets, create reliability in data used for decision-making and operation data synchronization. End to end metal accounting process visibility across the whole process will advance the audibility and transparency of metal accounting from mine to product, and enable good corporate governance and financial reporting of the salable metal product concerning smart mining and mineral processing concept.

Exploring process-level genotypic and environmental effects on sugarcane yield using an international experimental dataset

Crop improvement aims to produce high yielding genotypes for target environments. Crop models simulate yield formation as the outcome of a series of low-level processes, driven by environmental (E) variables and regulated by genetic (G) traits. There is potential for crop models to aid sugarcane breeding, by identifying desirable genetic traits for target environments. The objective of this study was to evaluate existing concepts of G and E control of plant processes for explaining crop development, growth and yield, using an international growth analysis dataset.Crop development, growth and yield were monitored in the plant and 1st ratoon crops for seven cultivars (N41, R570, CP88-1762, HoCP96-540, Q183, ZN7 and NCo376) grown under well-watered conditions at La Mare (Reunion Island, France), Pongola (South Africa (RSA), Chiredzi (Zimbabwe), and Belle Glade (Florida, USA). Weather data were collected and environmental conditions characterized for each experiment. Derived process-level phenotypic parameters, based on concepts from four sugarcane growth simulation models (DSSAT-Canegro, Mosicas, APSIM-Sugar and Canesim), were calculated from observations and used to (1) evaluate current understanding of E drivers of sugarcane growth and development processes, and (2) identify and quantify G control at a process level.Final yields showed significant E and GxE variation; dry above-ground biomass and stalk yields were highest in La Mare and lowest in Pongola. Cultivar rankings in stalk dry mass for the common cultivars (N41, R570, CP88-1762) varied significantly between Es. Significant E variation in phenotypic parameters describing germination, tillering and timing of the onset of stalk growth (OSG) revealed shortcomings in the underlying simulation concepts. Significant G variation was found for germination rate, leaf appearance rate and canopy development rate per unit thermal time (TT), and maximum radiation use efficiency, indicating strong G control of the associated underlying processes.Solar radiation was found to influence tillering rate per unit TT, and TT to OSG, challenging the current theory of TT as the sole driver of these processes.By explaining more of the E variation, more stable and accurate G-specific model parameters can be defined and evaluated. This is anticipated to lead to less GxE confounding of modelled processes, and hence crop models that are better-equipped for supporting sugarcane crop improvement.

Design and Development of Real-Time Plant Process Control Monitoring System in Organic Fertilizer Production

In this applied research, a plant controller with capacity of 300 litres has been developed. The system provides graphical user interface (graphics and tables) using Visual Basic program to show and monitor the fermentation process. Communication between the software and hardware is established through microcontroller’s interface. The microcontroller acts as the main controller which controls the humidity, temperature, pH, aerator, heater, exhaust fan, and the speed of the motor. Temperature and humidity sensor (DHT-22), Methane Gas sensor (CH), Carbon Gas sensor (MQ7), and Nitrogen Gas sensor (MiCS-2714) are used in this system. The domestic waste raw materials such as vegetables and sugar cane waste are thinly sliced while the fruits are grinded. Temperature, humidity, and pH readings are collected as reference parameters prior to the process starts. During the anaerobic digestion process, temperature is kept at 40°C ±1 while plant’s tube is maintained at 40°C. When the temperature falls to 39°C, the heater is switched on, when the temperature reach 40°C the heater is switched off. This research is carried on using two kind of mixture models which are: mixture model X (mixture of vegetables, fruits and sugar cane waste) and mixture model Y (same as X with additional rice husk). The mixture model X and Y C/N’s ratio is in compliance with SNI-7030-2004 standard. The real-time plant controller system proposed in this research is successfully.

Experimental study on the ignition characteristics of cellulose, hemicellulose, lignin and their mixtures

Ignition behaviour of biomass is an essential knowledge for plant design and process control of biomass combustion. Understanding of ignition characteristics of its main chemical components, i.e. cellulose, hemicellulose, lignin and their mixtures will allow the further investigation of ignition behaviour of a wider range of biomass feedstock. This paper experimentally investigates the influences of interactions among cellulose, hemicellulose and lignin on the ignition behaviour of biomass by thermogravimetric analysis. Thermal properties of an artificial biomass, consisting of a mixture of the three components will be studied and compared to that of natural biomass in atmospheres of air and nitrogen in terms of their ignition behaviour. The results showed that the identified ignition temperatures of cellulose, hemicellulose and lignin are 410 °C, 370 °C and 405 °C, respectively. It has been found that the influence of their interactions on the ignition behaviour of mixtures is insignificant, indicating that the ignition behaviour of various biomass feedstock could be predicted with high accuracy if the mass fractions of cellulose, hemicellulose and lignin are known. While the deficiencies of the determined mutual interactions would be further improved by the analytical results of the activation energies of cellulose, hemicellulose, lignin, their mixtures as well as natural and artificial biomass in air conditions.

Strengthening the efficacy of official food control improves Listeria monocytogenes prevention in fish-processing plants

Vacuum-packaged cold-salted and cold-smoked fish products are considered typical vehicles for Listeria monocytogenes, the causative agent of the food-borne disease listeriosis, which is increasingly prevalent in the European Union. Efficacy of both the fish processing plant self-checking system and official food control conducted by authorities are crucial for L. monocytogenes prevention in the processing of these risky products. However, the impact of official control on L. monocytogenes prevention in the processing of fish products has not been extensively studied. We investigated the occurrence, control measures, and correction of non-compliances predisposing to L. monocytogenes in Finnish fish processing plants. The following features were associated with L. monocytogenes occurrence: (a) frequency of non-compliances concerning processing machinery, (b) recurrence of non-compliances, and (c) frequency of non-compliances for which official control measures were requested by inspecting authorities. Official control of fish processing plants had focused on risky areas, but non-compliances were common and their correction exhibited deficiencies. We conclude that L. monocytogenes prevention in fish processing can be enhanced by strengthening official food control measures and processing plant compliance. In particular, timely correction of all food safety violations must be improved.

Data-driven self-optimizing control: constrained optimization problem

Self-optimizing control (SOC) is a technique used in selecting controlled variables (CVs) for a process plant control structure with a view to operating the plant optimally in the presence of uncertainties and disturbances. Existing SOC approaches are either local which result to large losses or too cumbersome to be applicable to real systems. In this work, a novel method of CV selection based on data was developed. In the method, a compressed reduced gradient of a constrained optimization problem was proposed to be estimated using finite difference scheme. The CV function was then used to approximate the necessary condition of optimality (NCO) using data only in a single regression step. The new approach was applied to a simplified case study and its performance was compared to an existing SOC methodology. An excellent goodness of fit was obtained during the regression with a R2-value of 1.0 associated with one of the designed CVs. The formulated CVs were found to be very robust with performance similar to that of NCO approximation method. A zero loss was incurred with one of the CVs.Keywords: compressed reduced gradient, constrained problems, controlled variable, data-driven, necessary condition of optimality, self-optimizing control

Quantitatively Understanding Plant Signaling: Novel Theoretical-Experimental Approaches.

With the need to respond to and integrate a multitude of external and internal stimuli, plant signaling is highly complex, exhibiting signaling component redundancy and high interconnectedness between individual pathways. We review here novel theoretical-experimental approaches in manipulating plant signaling towards the goal of a comprehensive understanding and targeted quantitative control of plant processes. We highlight approaches taken in the field of synthetic biology used in other systems and discuss their applicability in plants. Finally, we introduce existing tools for the quantitative analysis and monitoring of plant signaling and the integration of experimentally obtained quantitative data into mathematical models. Incorporating principles of synthetic biology into plant sciences more widely will lead this field forward in both fundamental and applied research.

Supervisory Fuzzy Expert Controller for Sag Mill Grinding Circuits: Sungun Copper Concentrator

ABSTRACTSuccessful control of Semi-Autogenous Grinding (SAG) mill circuits has been the subject of many researches and main concern of plant operators for years. Distributed Control Systems (DCS) have had some degree of success in the mineral processing circuits, but maintaining operation of a SAG mill on the “edge” of its full capacity is not easily achievable by DCS. Advanced control systems, however, are a relatively new opportunity to successful control of mineral processing plants. This article presents the basis of a supervisory fuzzy expert controller for SAG mill circuits. Although leading companies in control and automation have their own commercial packages, this supervisory controller is coded in Matlab using Mamdani method and is able to connect to plant lower level control system. In the proposed controller, fuzzy system calculates optimum set points to the plant DCS control loops, enabling them to change the manipulating parameters to reach the new set points. This controller was installed, tested and verified in a copper grinding circuit. Results showed 1.8% increase in mill throughput, 3% decrease in power draw and more stable feeding regime at the same time.

Problems Features of the Robust Control of Process Plants. Part I. Process Plants and their Mathematical Models

Problems Features of the Robust Control of Process Plants. Part I. Process Plants and their Mathematical Models

Plant cytokinin signalling

Cytokinin is an essential plant hormone that is involved in a wide range of plant growth and developmental processes which are controlled through its signalling pathway. Cytokinins are a class of molecules that are N(6)-substituted adenine derivatives, such as isopentenyl adenine, and trans- and cis-zeatin, which are common in most plants. The ability to perceive and respond to cytokinin occurs through a modified bacterial two-component pathway that functions via a multi-step phosphorelay. This cytokinin signalling process is a crucial part of almost all stages of plant life, from embryo patterning to apical meristem regulation, organ development and eventually senescence. The cytokinin signalling pathway involves the co-ordination of three types of proteins: histidine kinase receptors to perceive the signal, histidine phosphotransfer proteins to relay the signal, and response regulators to provide signal output. This pathway contains both positive and negative elements that function in a complex co-ordinated manner to control cytokinin-regulated plant responses. Although much is known about how this cytokinin signal is perceived and initially regulated, there are still many avenues that need to be explored before the role of cytokinin in the control of plant processes is fully understood.