Operation Of Industrial Plants Research Articles

Exploring the multiphase flow and mass transfer characteristics for desulfurization in an upflow biogas reactor

With the increasing requirements for biogas desulfurization, the LO-CAT process design needed to be optimized to meet the production requirements. The purpose of this study was to establish a joint optimization model based on two-film theory and response surface methodology (RSM) to investigate the gas-liquid flow, mass transfer, and chemical reaction processes of the biogas reactor and to obtain the optimal operating conditions. The model was validated based on the measured data of the biogas reactor in a 355 KW desulfurization unit at the landfill. It was revealed that the optimal parameters for the gas-liquid flow field were a particle diameter of 3100 µm, a liquid/gas ratio of 19 L/m3, a nozzle angle of 55°, and an inlet velocity of 16 m/s. The effect of inlet velocity was most significant in the LO-CAT process due to its internal extended entrance. The synergistic modulation between gas and liquid then improved the homogeneity of the flow field through vortices, increasing flow field uniformity by 20.8%. The absorption of H2S was closely related to the change in flow pattern; the mass transfer process was mainly limited by the "liquid membrane limitation." Optimized parameter desulphurization efficiency increased by 28.76%. This study provided practical significance for the rational design and optimized operation of industrial biogas purification plants based on multiphase flow reaction transfer.

A graph-based framework for complex system simulating and diagnosis with automatic reconfiguration

<abstract><p>In this work we present a novel approach for modeling complex industrial plants, employing directed graphs to the simulation and automatic reconfiguration after failures. The framework offers the possibility to model the failure propagation, estimating the overall condition of the system before and after the damage and exploit such a health index for dynamic recalibration. To model the typical operation of industrial plants, we propose several additions with respect to the standard graphs: <italic>i</italic>) a quantitative measure to control the overall condition of the system <italic>ii</italic>) nodes of different categories–and then different behaviors–and <italic>iii</italic>) a fault propagation procedure based on the predecessors and the redundancy of the system. The obtained graph is able to mimic the behavior of the real target plant when one or more faults occur. Additionally, we also implement a generative approach capable of activating a particular category of nodes in order to contain the issue propagation, equipping the network with the capability of reconfiguring itself and resulting in a mathematical tool useful not only for simulating and monitoring but also to design and optimize complex plants. The final asset of the system is provided in the output with its complete diagnostics and a detailed description of the steps that have been carried out to obtain the final realization.</p></abstract>

Multivariate Alarm Monitoring for Non-Convex Normal Operating Zones Based on Search Cones

Normal operating zones (NOZs) are the variation spaces of multiple related variables under normal conditions. This paper proposes a search-cone-based method to establish NOZ models and design dynamic alarm thresholds for multivariate alarm monitoring. First, mathematical models are established by exploiting search cones from historical normal data points to describe non-convex NOZs of multiple related process variables. Second, dynamic alarm thresholds of each process variable are designed based on the established NOZ models for new data points being inside or outside the NOZs. The proposed method extends from convex NOZs to non-convex ones, and the main contribution is to establish mathematical models of NOZs for monitoring abnormal conditions and design dynamic alarm thresholds for generating alarms. Continuous stirred tank reactors (CSTR) and industrial examples are provided to illustrate the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Alarm systems play critically important roles for safe and efficient operation of industrial plants. Many process variables are related so that their normal variation ranges formulate high-dimensional geometric spaces referred to as normal operating zones (NOZs). Hence, it is desirable to adopt the NOZs for alarm monitoring. By contrast, if relationships among process variables are ignored, missed and false alarms may occur to deteriorate the performance of alarm systems. This paper proposes a method to establish mathematical models for NOZs and design dynamic alarm thresholds. The established models can describe non-convex or convex NOZs; the dynamic alarm thresholds can quantify distances between NOZ boundaries and operating points of a multivariate process

Optimal Operation of Domestic and Industrial Sewage Treatment Plants Using Machine Learning Methods

Purpose: This study aims to determine the economic and technical feasibility of operating and leasing sewage treatment plants through an application that uses mathematical modeling and Machine Learning techniques for process optimization.   Theoretical Framework: Efficient operation of sewage treatment plants (STPs) is crucial to ensure water quality, minimize environmental impacts, and optimize costs. This study explores how Machine Learning (ML) can model and optimize sewage treatment processes, adapting to real-time conditions.   Method/Design/Approach: The BSM1 model is combined with Machine Learning techniques to create simplified metamodels, enabling optimized results and the development of an application for evaluating economic and technical outcomes.   Results and Conclusion: The reduced metamodel successfully reproduced the Simulink model, achieving satisfactory optimization.   Research Implications: This research benefits water quality improvement, cost reduction, sustainability, innovation, water resource management, awareness, and resilience to extreme weather events, as well as influencing informed policies.   Originality/Value: Efficiency, sustainability, economy, and quality of life are core values in this research, benefiting society, the environment, and the economy.

Sludge interface measurement in the storage tank utilizing neutron backscattering technique: A field experiment

Modern industrial plant operations commonly necessitate a measurement system of the liquids level interface in the oil storage tank. Various advanced-level indicators almost meet the demand, but these may not be in online condition and are less accurate due to sedimentation in the bottom tank. Sedimentation or sludge known as a petroleum hydrocarbons complex emulsion, contained water and solid particles generated during distribution, storage, or even production. The neutron backscatter technique can help the maintenance system of the tank or vessel by measuring sludge levels from outside the wall. This paper presents a non-intrusive method for identifying the interface between two materials in the crude oil storage system. A fast neutron source (241Am–Be) with activity 1 Ci (Ci) and a portable backscattering neutron detector are used for storage tank measurement. The storage tank has an outer diameter (OD) of 34 m and a height of 11 m. Time measurement is 2 s for each position, starting from 0 m to 11 m. The slow neutron intensity can be correlated to the hydrogen concentration inside chemical compounds. The interface between sludge and oil is identified at 27 cm from the bottom plate. The method has the potential for non-destructive measurement of sludge in a refinery facility.

Reduction of greenhouse gas emissions via flexible operation of cross‐sector integrated energy systems under uncertainties in demand, fuel prices, and solar irradiation

AbstractThis work investigates how the flexible operation of the light industrial plants integrated in a cross‐sector energy cluster with community energy systems can achieve further greenhouse gas (GHG) reductions under uncertainties associated with natural gas prices, solar irradiation, as well as heating, cooling, and electricity demand. The optimal flexible operation and design of a cross‐sector integrated cluster comprising a bakery plant, a brewery, a confectionery plant, a residential building, and a supermarket under uncertainties are compared to the operation and design of systems without uncertainties. When uncertainties are considered, the overall GHG emissions of the integrated system with steady industrial production rates for all uncertainty scenarios are over 4% higher than the integrated system in the deterministic scenario (a single scenario). Flexible operation of the industrial plants, whereby production rates are varied throughout the day, contributes an additional 3% reduction in GHG emissions under uncertainties, where the GHG emissions are only 1% higher than the deterministic scenario. Additionally, the system with flexible production rates purchases over 14.3% less electricity from the grid and uses over 72.2% less natural gas for operating the backup boiler, which relies less on supplementary energy resources. This shows that optimally designed integrated systems with flexible industry production schedules are resilient to uncertainties in energy demands, daily weather fluctuations, and fuel prices.

EJECTION WIND POWER PLANT

The article covers issues related to non-traditional sources of electricity, namely wind energy. It is shown that every year wind energy is used on an increasingly large scale in order to satisfy the need of mankind for reliable and environmentally friendly sources of energy. It is noted that in many European countries a significant amount of energy was produced in wind energy, which is approximately 2% of its global consumption. In the developed countries of Europe, wind energy already makes up a fairly significant share of all electricity. Compared to the countries of Western Europe and the Mediterranean, Ukraine is a windless region, where winds blow more stably only in the Carpathians and on the Azov coast. Despite this, a significant number of wind power plants have already been built in Ukraine. The wind speed for the operation of industrial wind power plants must be at least 3 m/s. As a result of weak wind or its temporary absence, wind turbines operate on average at 35% of the calculated capacity. The goal of the research is to develop an ejection wind power plant that can operate at 100% of the calculated capacity due to the creation of a constant upward flow of air. The article proposes the design of an ejection vertical wind power plant, which will ensure efficient operation and will be able to operate at 100% of the calculated capacity. The methodology uses an analytical research method, which is based on the calculations of the main indicators of the ejection power plant. Also, the methodology provides means of determining the speed of the air flow, which ensures the efficient operation of power plants. In the work, the calculations of the speed of the air flow on the ejection wind intake, the dynamic pressure of the fan, the impulse of the force of the air flow on the rotor blade, the projected power, and the payback period were performed. The indicators of the linear movement of the fairing, which is in the design of the ejection power plant, depending on the drop in atmospheric pressure, are given. The developed ejection wind power plant will ensure electronic independence, environmental protection, operational safety and economy.

TESTING HUMAN ERRORS IN VIRTUAL REALITY TRAINING

The emergency training of industrial process plant operators is one of the most widely used tools to increase the reliability of human factors to handle an emergency situation. However, the preparation and operation of full-fledged simulators and trainers is very expensive and, therefore, virtual environment tools are used. A question that has not yet been answered is: Can virtual reality match the reliability of other methods of operation and is the same training in virtual reality effective? The experiment was carried out in the three-walled virtual CAVE, with virtual reality glasses, with a computer, a tablet, and a real control panel. Visual stimuli were displayed on the screen of the virtual monitor (green, yellow, and red); auditory stimuli were pure tones with frequencies of 250, 1000 and 4000 Hz. The conclusion should explicitly state if the hypothesis defined for the research has been confirmed and there are significant differences in terms of interface type. Training in virtual reality induces lower operator reliability, but in specific conditions (visual stimuli, virtual reality glasses) can match the reliability of other methods of operation and can be effective.

Comparison of Fuzzy and Neural Network Computing Techniques for Performance Prediction of an Industrial Copper Flotation Circuit

This paper presents the development and validation of five different soft computing methods for flotation performance prediction: (1) two models based on fuzzy logic (Mamdani and Takagi-Sugeno fuzzy inference system) and (2) three models based on artificial neural networks. Copper content in the ore feed, collector dosage in the rougher and the scavenger flotation circuits, slurry pH in the rougher flotation circuit and frother consumption were selected as input parameters to estimate the copper grade and recovery of final concentrate, as well as the copper content in the final tailings of the flotation plant. The training and evaluation of the proposed models were performed on the basis of real process data collected by the multiannual monitoring of industrial flotation plant operation in “Veliki Krivelj Mine”. The results showed that the proposed soft computing-based models well describe the behavior of the industrial flotation plant in a wide range of circumstances. Among the proposed algorithms, artificial neural networks gave the most accurate predictions for the final copper concentrate grade and recovery (R2 = 0.98 and R2 = 0.99, respectively) and copper content in final tailings (R2 = 0.87). At some points, fuzzy logic models are almost equally efficient, but artificial neural networks had lower values for all error functions.

Analytical graphs to describe operating status of industrial alarm variables

A large number of alarm variables are configured to automatically detect the occurrences of abnormalities at complex industrial plants. As abnormalities are absent, occurring, persisting and disappearing, alarm variables go through the normal status, running into the abnormal status, staying in the abnormal status, and leaving the abnormal status. This paper formulates novel analytical graphs to describe the operating status of single and multiple alarm variables. By looking at the analytical graphs, industrial plant operators are aware of the status of alarm variables in a concise and clear manner, instead of via a high-demanding approach by visualizing massive operation data. The analytical graphs are composed of standard and newly-defined status metrics. Typical patterns of analytical graphs are summarized for different status of alarm variables. The effectiveness of formulated analytical graphs is illustrated by numerical and industrial examples.

A maximum-entropy-based method for alarm flood prediction

Alarm floods are the phenomena of presenting too many alarms in a short time period to exceed abilities of industrial plant operators in making proper responses. This paper proposes a maximum-entropy-based method to predict upcoming alarms for an occurring alarm flood. The proposed method has two important features: all currently-occurred alarms are exploited for prediction, and upcoming alarms are given with quantitative probabilities. By contrast, existing alarm prediction methods either use most-recent (not all) occurred alarms in prediction, or cannot predict specific alarms with quantitative probabilistic values. The proposed method takes all historical alarm flood sequences into account to establish relationships between currently-occurred alarms and upcoming alarms, and formulates an optimization problem to maximize conditional entropies of upcoming alarms. The effectiveness of the proposed method is validated by numerical examples, one of which is on the well-accepted benchmark of Tennessee Eastman process.

Robust control and training risk reduction for boiler level control using two-stage training deep deterministic policy gradient

The stability of the boiler drum level is important for safe and stable operation of industrial plants. In this study, a two-stage training deep deterministic policy gradient (2S-DDPG) comprising offline pretraining and online training was proposed to control the boiler drum level. A comparison of simulation results between the 2S-DDPG, DDPG, and 3E training methods proved that 2S-DDPG can robustly control the boiler drum level. The 2S-DDPG model requires less than half as much interaction with online processes as DDPG does; this ensures stable industrial operation due to the lowered risk of process failures in training. The results indicated the integral absolute error of the three-step 2S-DDPG is the lowest among those of the three control models. Moreover, the three-step 2S-DDPG reduced the overshoot percentage calculated using 3E control from 59% to 0%. For processes with noise and time delay, 2S-DDPG exhibits a faster response and less variation in the control performance with regard to the boiler drum level. The manipulated variable distribution errors of the three-step 2S-DDPG were much less than those of the DDPG model. Therefore, 2S-DDPG can address the shortcomings of the traditional DDPG model.

Short Term Planning and Scheduling For Gasoline Blending in Oil Refineries

Product blending is an important optimization task that is encountered in the operation and scheduling of important industrial plants like petroleum refineries. The key objective of blending is to mix various intermediate products to achieve desired properties and quantities of products with minimum cost. There are uncertain parameters which make it very difficult to attain the optimum allocation of available resources. Consequently, there is a need to develop computational optimization techniques to tackle the blending issues. &#x0D;    In this research the main objective is to propose an approach to solve product blending issue in an optimum&#x0D; way. The blending problem can be formulated as an optimization where its objective is to maximize net profit while determining the optimal allocation of intermediate streams to produce optimum production mix of final products.&#x0D;    The proposed approach is introduced for integrating short term planning and scheduling for product blending. Two mathematical models have been proposed. The first model deals with planning issue for product blending and the results are regarded as production guidelines. In the second scheduling model, scheduling will treat the production guidelines to verify optimum allocation for available resources. The approach was applied to different real time case studies form Midland Refineries Company, and the results show the efficiency and flexibility of this approach to solve the different case studies. Also minimize lead time from 72 to 24 hr in the second case according to reduction in re-blend process, in addition to minimize production cost depending on optimum allocation for available resources. The last case study which is a complicated one, were WIN QSB version 1.00 software is utilized. The results gained after 0.031 second CPU time for planning level and 3.375 second CPU time for scheduling level. This is considered as an advantage to the model. 

Systematic simplification of the Anaerobic Digestion Model No. 1 (ADM1) – Model development and stoichiometric analysis

Rigorous process models provide a reliable basis for model-based monitoring and control of anaerobic digestion plants. Due to the complex model structure and non-linear system characteristics, the established Anaerobic Digestion Model No. 1 (ADM1) is rarely applied in industrial plant operation. The present investigation proposes a systematic procedure for successive model simplification and presents the description of five model variants of a mass-based ADM1. Individual model structures greatly differ in their number of implemented process phases, characteristic components and required parameters. Simplified model variants combine nutrient degradation and biogas formation based on first-order sum reactions, whereas complex model structures describe individual degradation pathways and intermediates during acido- and acetogenesis. Characteristic features of the derived model structures as well as the stoichiometric methane potentials and microbial biomass yields of the underlying degradation pathways of individual model variations are evaluated and discussed in detail.

The Foundations of Civil Liability for Industrial Pollution

Abstract In European private law, operators of industrial facilities, power plants and other sites using special substances or procedures are made responsible for harm caused by pollution even where it is doubtful that such harmdoing is unreasonable or could have been foreseen. Analysing both fault-based and strict liability, the author discusses legal bases for this liability and its justification in European jurisdictions.

The use of terrestrial laser scanning in the preservation of valuable architectural objects

In the present day, we are witnessing the dynamic development of our country. We observe a growing number of new construction investments, which are designed to meet the needs of the market. Streets are being widened to cope with the growing number of vehicles, modern office buildings and skyscrapers are being built in the largest Polish cities, which at the same time have valuable architectural objects in their oldest districts. Such objects, due to their age, are susceptible to damage, and thus to the threat that their value will be lost. Such damage may occur in the course of construction works that destabilize the soil structure, which may lead to damage to the building’s foundations and, as a result, harm or destroy the most important structural elements of the monument. Another important factor is the operation of industrial plants that emit harmful substances, which have a negative impact on façades and other external elements, such as, for example, relief sculptures. It may be difficult and complicated to remove the effects of the risks described above if the documentation necessary to carry out protection or renovation works is incomplete or insufficiently detailed. A separate issue worth discussing are architectural objects made of perishable materials such as wood [Bernat et al. 2014]. There are many objects of wooden architecture in Poland, such as: Catholic and Orthodox churches, open-air museums, and other relics of bygone eras. Apart from the obvious threat of fire and its negative effects, one can also mention the negative impact of precipitation, whether in the form of rain causing the wood to soak and, as a result, to rot, or the risk of damaging the foundations during a flood. The listed threats have a direct and indirect impact on the structure of such historical buildings. Therefore, it is important to take care of their detailed survey, with the view to preserving and maintaining them. It is also worth mentioning a large number of castles located in our country. The condition of their structures is very diverse and ranges from newly restored buildings to those with only foundations left. In all cases, it is important to obtain accurate plans and models of these building objects. This will serve to preserve their dimensions and shapes. Such data can be used to develop documentation necessary to carry out reconstruction or renovation in order to return the building to its former glory, and thus obtain another object worth seeing.

Prediction of reflection amplitudes for ultrasonic inspection of rough planar defects

The characteristics of planar defects (no loss of material volume) that arise during industrial plant operation are difficult to predict in detail, yet these can affect the performance of non-destructive testing (NDT) used to manage plant structural integrity. Inspection modelling is increasingly used to design and assess ultrasonic inspections of such plant items. While modelling of smooth planar defects is relatively mature and validated, issues have remained in the treatment of rough planar defect species. The qualification of ultrasonic inspections for such defects is presently very conservative, owing to the uncertainty of the amplitudes of rough surface reflections. Pragmatic solutions include the addition of large sensitivity thresholds and more frequent inspection intervals, which require more plant downtime. In this article, an alternative approach has been developed by the authors to predict the expected surface reflection from a rough defect using a theoretical statistical model. Given only the frequency, angle of incidence and two statistical parameter values used to characterise the defects, the expected reflection amplitude is obtained rapidly for any scattering angle and size of defect, for both compression and shear waves. The method is applicable for inspections of isotropic media that feature surface reflections such as pulse-echo or pitch-catch, rather than for tip signal-dependent techniques such as time-of-flight diffraction. The potential impact for inspection qualification is significant, with the new model predicting increases of up to 20 dB in signal amplitude in comparison with models presently used in industry. All mode conversions are included and rigorous validations using numerical and experimental methods have been performed. The model has been instrumental in obtaining new statistically significant results related to the effect of tilt; the expected pulse-echo backscattered amplitude for very rough planar defects is independent of tilt angle, with convergence obtained for a range of frequencies.

Self-Evaluation of Industrial Case Studies with Iterative Improvements to Support Chemical and Biological Systems Engineering and Sustainability Teaching and Learning

Industrial case studies play an increasingly important role in the teaching of systems engineering and sustainability within an industrial ecological framework utilising metacognitive and experiential learning principles. The principal classroom teaching tool of the experiential learning combines the student’s development of self-awareness and selfevaluation skills whilst they focus on providing optimal solutions to challenges provided by industrial case study examples. The learning process here relies on developmental and experiential learning with students attempting at iterative improvements to the industrial processes and plant operations being studied with a view to create a “best fit” to the efficiency of use of the resources at hand, specific ecological setting and circular economy constraints regarding recycle, re-use and regeneration of resources. Two alternative approaches of iterative case study evaluations are presented and compared here; one relying on the “bottom-up” approach to systems identification and development to enable the use of new material and energy resources and the other on a “top-down” approach to evaluate and improve an existing system of complex and integrated process plant operations. In each case, the student is challenged in an increasing order of complexity of issues through a self-learning and evaluation process which also requires the necessary deepening of engagement with the normative knowledge base comprising of core engineering and applied science teaching curricula. The additional benefit of the student-centered teaching and learning (see also Tuzun 2020 [8]) is the ability to draw upon core scientific and engineering science “hands-on”; as opposed to classic passive learning by lectures and tutorials in advance of any case study applications to real-life challenges.

A Novel Approach to Defining Maintenance Significant Items: A Hydro Generator Case Study

The operation and maintenance of industrial plants constitute a major concern among managers, scientists, and engineers. To allow the prioritization of maintenance actions for improved continuous equipment operation, this paper presents a novel method focused on identifying the most critical components of a plant’s systems. In a complex engineering approach, this method uses tools and techniques borrowed from several fields of knowledge, including an innovative technique devised by the authors, as well as a multi-criteria decision method in the criticality determination process. To illustrate the method, a case study is presented considering a 150 MW Kaplan hydro generator from a hydroelectric power plant located in the north of Brazil. An uncertainty analysis is presented, considering variations in the scores of each criterion, to verify the robustness of the proposed method. The results show that the method responds in a solid and structured way, allowing decision making from qualitative and quantitative data and eliciting the opinion of experts on the factors that can affect the degradation of the performance of a system. In addition, the method’s robustness is confirmed since the uncertainty propagation related to the criteria scores is small for the items classified as most significant from its application.

Data-driven optimal operation of the industrial methanol to olefin process based on relevance vector machine

Methanol to olefin (MTO) technology provides the opportunity to produce olefins from nonpetroleum sources such as coal, biomass and natural gas. More than 20 commercial MTO plants have been put into operation. Till now, contributions on optimal operation of industrial MTO plants from a process systems engineering perspective are rare. Based on relevance vector machine (RVM), a data-driven framework for optimal operation of the industrial MTO process is established to fully utilize the plentiful industrial data sets. RVM correlates the yield distribution prediction of main products and the operation conditions. These correlations then serve as the constraints for the multi-objective optimization model to pursue the optimal operation of the plant. Nondominated sorting genetic algorithm II is used to solve the optimization problem. Comprehensive tests demonstrate that the ethylene yield is effectively improved based on the proposed framework. Since RVM does provide the distribution prediction instead of point estimation, the established model is expected to provide guidance for actual production operations under uncertainty.