Advanced Process Control

Intensive developments in advanced control techniques have opened up novel possibilities for food process control. Model-based control, distributed control systems together with field communication protocols, and other computer-aided advanced control strategies, such as fuzzy logic and neural networks, are already widely used in process engineering. The benefits of advanced control techniques include reduced costs, increased quality, and improved safety. This paper presents the potential of such novel control tools in drying of agricultural products.

This paper presents a cross-platform validation methodology for Model Predictive Control (MPC) systems in nuclear applications, using open-source tools. The approach employs a dual framework validation strategy: a commercially validated MATLAB/Simulink implementation serves as the reference benchmark, while an equivalent Python-based implementation is systematically verified. The methodology is demonstrated through a steam generator control case study for a Low Temperature Reactor (LTR), using a simplified lumped parameter model linearized at five operating points. Controller performance was quantitatively compared across 13 transient scenarios using Normalized Root Mean Square Deviation (NRMSD), with all key outputs showing NRMSD < 0.5%. The MPC strategy reduced water level overshoot by 48% and settling time by 62% compared to conventional PID control, while effectively handling constraints and non-minimum phase dynamics. Results confirm that open-source platforms can serve as credible environments for prototyping nuclear grade control algorithms, providing a transparent pathway from design to deployment.

A detailed empirical comparison of process control algorithms will be available in this paper with special emphasis on classical Proportional-Integral-Derivative (PID) control, Model Predictive Control (MPC), and machine learning-based solutions in the framework of complicated industrial plants. Because of the rising number of years that industries require in terms of operational efficiency, product quality, and safety, there is an urgent need to accommodate any capacity through strong and versatile control systems. The study used a real-life dataset and a simulated refinery dataset of the Tennessee Eastman (TE) process to benchmark the performance of various control strategies on diverse types of disturbances and conditions. The main research topics concern the description of the advantages of algorithms, the methodology to use in fair comparisons, and the consequences of this discussion regarding the application in practice. Due to thorough quantitative analysis, including the variables of the Integral of Squared Error (ISE) and Integral of Absolute Error (IAE), the results show that modern controllers, and neural network ones in specific, are superior to conventional designs of PID or MPC, as they have better disturbance rejection rates and more accurate tracking of set values. Nonetheless, the operational and safety difficulties of incorporating and implementing improved algorithms must be well mitigated to improve the use of the algorithms in safety-critical settings. The latter findings show that women in this comparative analysis contribute to the theoretical knowledge process control and provide recommendations that industry actors can take to successfully combine the advanced control methods. This study forms the basis of the subsequent developments in the field of empirical process control assessment. It emphasizes the possibility of a hybrid model that could successfully synthesize the best aspects of classical and modern evaluation tactics.

UMngeni-uThukela Water (UUW) is a provincial water board responsible for bulk water supply and wastewater services across KwaZulu-Natal (KZN), South Africa. The organisation operates a geographically dispersed portfolio of water and wastewater treatment facilities, supported by varying levels of automation, including Supervisory Control and Data Acquisition (SCADA) and Distributed Control Systems (DCS). However, a significant number of smaller facilities remain manually operated, limiting real-time monitoring, operational efficiency, and system-wide integration. UUW is responsible for the management, operation, and monitoring of more than twenty water treatment facilities, over ten wastewater treatment plants, as well as laboratories and administrative office buildings. The scale and geographical distribution of these assets present significant operational challenges for UUW. One of the primary challenges facing UUW is the effective integration of advanced technological systems to enable seamless control, monitoring, and decision-making across all facilities. The existing system architecture (2025) lacks full integration with modern digital technologies, limiting real-time data visibility and coordination between facilities. As a result, critical functions such as material and energy balance calculations cannot be performed efficiently or within short timeframes. This constraint reduces the organisation's ability to optimise process performance, manage resources effectively, and respond promptly to operational deviations. In alignment with Industry 4.0 principles, this study evaluates the implementation of Advanced Process Control (APC) systems within UUW operations. The proposed approach integrates treatment works, bulk distribution networks, catchment monitoring, and support services into a unified, hierarchical control architecture. Technologies such as Multivariable Predictive Control (MPC), real-time optimisation, and integrated data platforms are considered as key enablers. The study demonstrates that adopting APC systems-supported by technologies such as Multi-Variable Predictive Control (MPC), real-time optimisation, and integrated data platforms-can significantly improve process stability, reduce energy consumption, minimise water losses, and enhance decision-making. Lessons from sectors such as petrochemical and logistics industries highlight the benefits of fully integrated operational and planning systems. A phased implementation approach is proposed, beginning with the automation of manually operated plants, followed by integration into regional and provincial control centres. The study further explores the transition towards Level 4 digital maturity, incorporating Machine Learning (ML) and Artificial Intelligence (AI) for predictive and autonomous operations. Findings indicate that the adoption of APC systems can significantly enhance process stability, reduce energy consumption, minimise water losses, and improve operational decision-making. Ultimately, the implementation of an integrated control architecture has the potential to transform water service delivery in KZN by improving efficiency, reducing operational costs, and enhancing sustainability across the water value chain.

A typical refrigeration system consists of several basic components such as compressors, condensers, expansion devices, evaporators, in addition to several accessories such as controls, filters, driers, oil separators etc. For efficient operation of the refrigeration system, it is essential that there be a proper matching between various components. Before analyzing the balanced performance of the complete system, it is essential to study the design and performance characteristics of individual components. Except in special applications, the refrigeration system components are standard components manufactured by industries specializing in individual components. Generally, for large systems, depending upon the design specifications, components are selected from the manufacturers' catalogs and are assembled at site. Even though most of the components are standard off-the-shelf items, sometimes components such as evaporator may be made to order. Small capacity refrigeration systems such as refrigerators, room and package air conditioners, water coolers are available as complete systems. In this case the manufacturer himself designs or selects the system components, assembles them at the factory, tests them for performance and then sells the complete system as a unit. Refrigeration and Air Conditioning (AET 606) Aerospace Department Fall 2025 ___________________________________________________________________________ Refrigeration and Air Conditioning (AET 606) Aerospace Department Fall 2025 ___________________________________________________________________________ 4 | P a g e Aspect Positive Displacement Compressor Dynamic Compressor Working Principle Traps a fixed volume of gas and compresses it by reducing its volume Increases gas velocity and converts it into pressure Common Types Reciprocating, Screw, Scroll Centrifugal, Axial Pressure Output High pressure at low to medium flow rates Medium to low pressure at high flow rates Flow Rate Low to medium High Efficiency with Load Variations High efficiency under variable loads Lower efficiency with fluctuating loads Applications Refrigerators, small HVAC systems, industrial high-pressure systems Power plants, large HVAC systems, turbines Pressure Control Simple and precise Requires complex control systems Higher cost per pressure unit but easier maintenance Lower cost per power unit but more complex maintenance For refrigeration capacity figure (2) presents a logarithmic-scale comparison of refrigeration capacities for various compressor types, ranging from small residential units to large industrial systems. Rolling piston compressors operate between 0.25 and 22 kW, suitable for compact applications. Scroll compressors range from 6.5 to 100 kW, commonly used in medium HVAC systems. Rotary vane compressors cover 7 to 300 kW, though they are less common. Reciprocating compressors, with a wide span from 0.25 to 1000 kW, are highly versatile and found in many applications. Screw compressors range from 75 to 6000 kW and are ideal for industrial environments requiring continuous operation. Turbo (centrifugal) compressors dominate high-capacity applications, operating between 270 and 36,000 kW, making them essential in large-scale cooling such as district systems or power plants. Each type is optimized for a specific range, balancing efficiency, cost, and application scale.

The five-volume set (Volumes 1-5) encapsulates the forefront of petroleum refining advancements.  These volumes intricately expound on cutting-edge refining processes and advanced technologies, encompassing crucial elements like hydrocarbon fluid pipe sizing, precision instrumentation design (including orifice, control valves and relief valves), and meticulous flare and vessel sizing.

The exploration extends to compressible and two-phase fluid flow of dynamic within process pipelines, with detailed insights into the design nuances of two and three phase vessels.  Mass transfer phenomena and design principles of distillation columns, absorption and liquid-liquid extraction columns are comprehensively covered, along with meticulous analyses of heat transfer operations and diverse exchanger types.

A pivotal focus on process integration within intricate heat exchanger networks is evident, harmonizing seamlessly with strategic product blending approaches and the classification of crude oils using ternary diagrams.  The volumes also dissect operational costs considering the global impact and economic aspects of refining, including carbon taxation, global warming, carbon capture technologies, and sustainability paradigms.

An indelible aspect is the integration of insights from real-world process safety incidents documented by the US Chemical Safety and Hazard Investigation Board (www.csb.gov/videos). These incidents are meticulously analyzed, yielding prudent recommendations that underscore the paramount importance of safety.  Practicality is heightened through furnished case studies and examples, aided by the utilization of Excel spreadsheets and Honeywell UniSim Design software tools.

Culminating in a comprehensive glossary of Petroleum and Petrochemical Technical terminologies, this compendium stands as an indispensable guide for engineers, scientists, and scholars.  It distills the zenith of petroleum refining knowledge into a concise, invaluable resource.

This paper considers a solution of the task of optimal control of a complex processing systems by economical criteria such as cost, profit and output of final product. At the first stage, the optimal values of controlled process parameters are defined by using step by step steady-state optimization method designed by the authors and taking into account features of economical criteria. At the second stage, a maintenance of suggested parameters is implied by using digital minimum variance controller.

The paper aims to revisit and discuss an application of advanced process control completed 7 years ago at a Delayed Coking Unit at the Agip Petroli Gela refinery. The application had some remarkable features, including: • Multi-layered strategy featuring different technologies working in synergy (MPC, Neural networks, SPC, etc.) • Completely DCS-embedded solution • Ad-hoc logics to build “virtual” feedforward variables able to detect process disturbances The number of years of continued operation allows examining the actual benefits that applying high-tech control strategies deliver to a complex unit like a delayed coking. It represents also an useful opportunity to re-think and assess which lessons have been learnt and how a similar project could be realized today, with latest generation tools and methodologies. composite After a quick overview about the implementation details, the paper will assess the results and the performance on a pretty long time-span (7 years!). A final sect...

The conventional steam power plant working under the rankine cycle and steam condenser as a heat sink and steam boiler as a heat source have the same importance for the power plant operating process. Energy efficiency of any power plant strongly depends on its turbine-condenser system operation mode. Operating the condenser at optimum circulation water flow rate is essentially important to ensure maximum efficiency and minimum operating cost of the plant. To control the condenser variables like cooling water flow rate, condenser pressure, condenser temperature having vital importance on entire plant performance. For the given power plant configuration, cooling water temperature or/and flow rate change generate alterations in the condenser pressure. Those changes have great influence on the energy efficiency of the plant. This paper focuses on the influence of the cooling water temperature and flow rate on the condenser performance..

The proposed algorithm of extended predictive control ͑EPC͒ represents an exact method for removing the ill-conditioning in the system matrix by developing a unique weighting structure for any control horizon. The main feature of the EPC algorithm is that it uses the condition number of the system matrix to evaluate a single tuning parameter that provides a specified closed-loop response. Robust analysis demonstrated that EPC is more robust in comparison with move-suppressed and m-shifted predictive controllers in all aspects of process variation in gain, delay, and time-constant ratios. Tuning of EPC is effective and simple since there is a direct relationship between closed-loop performance and its tuning parameter.

This LDRD activity used hydrogen that was produced by a separate high-temperature steam electrolysis experiment supported by the U.S. Department of Energy's Nuclear Hydrogen Initiative. As part of this collaboration, hydrogen produced during 2500 hours of continuous operation of a solid oxide electrolysis cell was supplied as a feed stream to the experimental setup established under this LDRD project. The integration of these two experiments to establish a "hybrid" energy system is considered to be part of the current effort; however, the testing activities and publication of data from the electrolysis demonstration is not published in this report, and should not be credited to this activity.

The article provides an overview of an Advanced Process Control System (APCS), focusing on a specific implementation within the Sakhalin-1 project. Initially, the project used a third- party APC, AspenTech DMC, which was layered on top of the existing Emerson DeltaV Distributed Control System (DCS). This implementation successfully increased oil production and reduced greenhouse gas emissions. However, as part of a strategic optimization initiative, it was determined that the APCS could be natively implemented using the existing Emerson DeltaV hardware and software. This integrated approach significantly reduces project costs and timelines by eliminating the need for third-party interface implementations, associated licensing and administrative expenses, and separate long-term support models.

In order to ascertain the quality of drinking water of the city of Hyderabad one of the significant parametric values of the drinking water was predicted. Like other parameters Electrical Conductivity (EC) is also imperative. The determination of electrical conductivity provides a prompt and expedient way to measure the accessibility of electrolytes in the water. There are swayed health effects on human life through these electrolytes, like disorder of salt and water balance in infants, heart patients, individuals with high blood pressure, and renal diseases. Salty taste is one of the aesthetic effects of EC if it exceeds 150 mS/m and if greater than 300 mS/m it does not slake the thirst. The drinking water supplied to Hyderabad city is taken from River Indus and the EC of this river remains questionable. The values of EC in drinking water of Hyderabad at selected locations were recorded. From 49 samples, the average values ranged from 658 to762. In order to determine the optimal value of EC with in the distribution system, where it deteriorates, it is necessary to predict it at different locations. The use of conventional methods to predict parametric values in the distribution systems is suffered from certain precincts. To get better drinking water quality by tumbling operational costs, Advance process control and automation technologies are the tools to be used normally. The WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT

Applied Materials Rapid Thermal Processing (RTP) systems are unique in providing high resolution process data particularly wafer rotation angle and wafer rotation speed as a function of time. This work explores how this information can be used to predict on-wafer process results using an advanced analysis package known as WISR (Wafer Interdiction and Scrap Reduction). WISR is an advanced process control platform for the collection, storage, visualization, and analysis of process parameters from production tools. One of the main analysis features of WISR is the ability to create virtual sensors. Virtual sensors are calculated parameters derived from physical sensors that can provide real-time and statistical representation of process health. The focus of this paper is the ability of WISR to transform time series chamber parameters from the pyrometers and the magnetic levitation controller into thermal wafer images at any time during the recipe execution and provide wafer-to-wafer handoff correction. These abilities constitute a virtual sensor module in WISR known as Virtual Metrology (VM). We describe the implementation of the VM-analysis and show how temperature maps and handoff corrections correlate with offline metrology in RTP critical anneals. This is an innovative new method to significantly reduce wafer processing errors, enhance yield, and minimize production cost.

A portable, non-cryogenic, oxygen generation system capable of delivering oxygen gas at purities greater than 98% and flow rates of 15 L/min or more is described. The system consists of two major components. The first component is a high efficiency membrane capable of separating argon and a portion of the nitrogen content from air, yielding an oxygen-enriched permeate flow. This is then fed to the second component, a pressure swing adsorption (PSA) unit utilizing a commercially available, but specifically formulated zeolite compound to remove the remainder of the nitrogen from the flow. The system is a unique gas separation system that can operate at ambient temperatures, for producing high purity oxygen for various applications (medical, refining, chemical production, enhanced combustion, fuel cells, etc . . . ) and represents a significant advance compared to current technologies.

In many chemical engineering process control applications, one frequently encounters differential-algebraic optimization problems. Such optimal control problems are difficult to solve, in general, because of the presence of singular arcs for systems whose Hamiltonian is linear with respect to the control variable. We propose the use of absolute error penalty functions (AEPF) in handling constrained optimal control problems in chemical engineering by posing the problem as a nonsmooth dynamic optimization problem. We show that Iterative dynamic programming (JDP) is a very useful technique for solving constrained dynamic optimization problems without unduly increasing the dimension of the system or the computational burden. A move suppression criterion has been incorporated into the IDP algorithm in order to penalize excessive control moves. To show the efficacy of the method, an analytical (exact) solution of a simple problem is obtained using least squares control theory and compared with results obtained using IDP. Results obtained for other seemingly difficult optimal control problems in chemical engineering compare very favourably with those reported in the optimization and optimal control literature.

In many chemical engineering process control applications, one frequently encounters differential-algebraic optimization problems. Such optimal control problems are difficult to solve, in general, because of the presence of singular arcs for systems whose Hamiltonian is linear with respect to
the control variable. We propose the use of absolute error penalty functions (AEPF) in handling constrained optimal control problems in chemical engineering by posing the problem as a nonsmooth dynamic optimization problem. We show that Iterative dynamic programming (IDP) is a very useful technique for solving constrained dynamic optimization problems without unduly increasing the dimension of the system or the computational burden. A move suppression criterion has been
incorporated into the IDP algorithm in order to penalize excessive control moves. To show the efficacy of the method, an analytical (exact) solution of a simple problem is obtained using least squares control theory and compared with results obtained using IDP. Results obtained for other seemingly difficult optimal control problems in chemical engineering compare very favorably with those reported in the optimization and optimal control literature.

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District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, prolonging the investment return period. The main scope of this paper is to assess the feasibility of using the heat demand -outdoor temperature function for heat demand forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations.

This paper presents two advanced control techniques that can be used to optimize the operation of seawater reverse osmosis (SWRO) desalination plants to decrease the production cost. The first technique uses the model predictive control (MPC) controller to operate the plant at its optimum operating conditions, while the second technique is based on early detection of membrane fouling. The objective of this work is to compare the SWRO desalination plant optimized performance with the conventional operation and evaluate the optimization effect on the unit production cost as well as conduct adequate economical analysis. Three case studies including three existing SWRO plants were tried and compared using the above technique. The second technique proved to be effective and the results were compared with the existing data and proved to be satisfactory.

Plasma etching is a semiconductor manufacturing process during which material is removed from the surface of silicon wafers using gases in plasma form. A host of chemical and electrical complexities make the etch process notoriously difficult to model and troublesome to control. This work demonstrates the use of a real-time model predictive control scheme to maintain a consistent plasma electron density in the presence of disturbances to the ground path of the chamber. The electron density is estimated in real time using a virtual metrology model based on plasma impedance measurements. Recursive least squares is used to update the controller model parameters in real time to achieve satisfactory control of electron density over a wide operating space.

In semiconductor manufacturing advanced process control (APC) refers to a range of techniques that can be used to improve process capability. As the dimensions of electronic devices have decreased, the application of APC has become more and more important for the critical stages of production processes. However, the economic disadvantage of employing APC is that it requires feedback information in the form of downstream metrology data, which is both time consuming and costly to obtain.

Plasma etch is a semiconductor manufacturing process during which material is removed from the surface of semiconducting wafers, typically made of silicon, using gases in plasma form. A host of chemical and electrical complexities make the etch process notoriously difficult to model and troublesome to control. This work demonstrates the use of a real-time model predictive control scheme to control plasma electron density and plasma etch rate in the presence of disturbances to the ground path of the chamber. Virtual metrology (VM) models, using plasma impedance measurements, are used to estimate the plasma electron density and plasma etch rate in real time for control, eliminating the requirement for invasive measurements. The virtual metrology and control schemes exhibit fast set-point tracking and disturbance rejection capabilities. Etch rate can be controlled to within 1% of the desired value. Such control represents a significant improvement over open-loop operation of etch tools, where variances in etch rate of up to 5% can be observed during production processes due to disturbances in tool state and material properties.

In order to ascertain the quality of drinking water of the city of Hyderabad one of the significant parametric values of the drinking water was predicted. Like other parameters Electrical Conductivity (EC) is also imperative. The determination of electrical conductivity provides a prompt and expedient way to measure the accessibility of electrolytes in the water. There are swayed health effects on human life through these electrolytes, like disorder of salt and water balance in infants, heart patients, individuals with high blood pressure, and renal diseases. Salty taste is one of the aesthetic effects of EC if it exceeds 150 mS/m and if greater than 300 mS/m it does not slake the thirst. The drinking water supplied to Hyderabad city is taken from River Indus and the EC of this river remains questionable. The values of EC in drinking water of Hyderabad at selected locations were recorded. From 49 samples, the average values ranged from 658 to762. In order to determine the optimal value of EC with in the distribution system, where it deteriorates, it is necessary to predict it at different locations. The use of conventional methods to predict parametric values in the distribution systems is suffered from certain precincts. To get better drinking water quality by tumbling operational costs, Advance process control and automation technologies are the tools to be used normally. The WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT

In this research, feedforwardANN (Artificial Neural Network) model is developed and validated for predicting the pH at 10 different locations of the distribution system of drinking water of Hyderabad city. The developed model is MLP (Multilayer Perceptron) ...

arge, single train hydrogen plants, with a capacity in the range of 60 200 mmscfd are becoming increasingly prevalent. The economies of scale from supplying multiple customers, synergies with energy integration and proven reliability have all boosted their popularity. However, the characteristics of large capacity hydrogen plants present greater challenges than medium sized (&lt; 60 mmscfd) plants and demand a different approach in their design and operation. Such challenges require stronger emphasis on design boundaries, reliability valuation, utilities integration and effective execution strategies, as well as a stronger commitment to efficiency, environmental compliance and overall economics. Moreover, these large facilities generally demand tighter execution schedules and rapid commissioning, while facing the familiar project budget pressures and site specific (such as cold climate) constraints. For Technip and Air Products there are four major challenges that must be addressed ...

Thermal regulation is based on the ability of metals to change their-geometric dimensions and volume when temperature changes. This type of regulation can be either external or internal and is carried out in relation to the working tool or the material being processed. The type of control under consideration is inertial and can be used for the ¬finishing group of the mill. The advantage of this method is the ability to influence ¬asymmetrical flatness defects during the rolling process. The use of forced cooling systems for rolled strips, which include interstand cooling (ISC) in the finishing group of stands and accelerated cooling on the outgoing roller table, can increase the productivity of mills and improve the consumer properties of hot-rolled strips.

The approach for dynamic business process management, based on the use of graphical notation with the combined control and data networks, is considered. Due to the explicit instructions of the data stream it is possible to overcome the automatic semantic gap that arises between the graphical and executable model of business processes. This leads to an increase in the quality control of production and technological processes in enterprises.

This paper presents a computational approach to analyzing the air-standard Otto cycle, widely used in gasoline engines. The paper develops a Python-based model to calculate key thermodynamic parameters, generate Pressure-Volume (PV) diagrams, and assess thermal efficiency. By simulating compression, combustion, and expansion phases, the study provides insights into the efficiency and performance of spark-ignition engines. The work serves as a practical resource for engineers and students, offering a visual and analytical understanding of Otto cycle behavior and its role in internal combustion engine design.

CMOS 65nm technology node requires the introduction of advanced materials for critical patterning operations. The study is focused on the multilayer Anti Reflective Coating (ARC) stack, used in photolithography, for the gate patterning such as Advanced Patterning Film (APF). The interest on this new and complex ARC stack lies in the benefit to guarantee low CD dispersion thanks to a better reflectivity control and resist budget which leads to a larger lithographic process window. However, it implies numerous metrology challenges. The paper deals with the challenges of monitoring the gate Critical Dimension (CD) on this stack. The validation of the scatterometry model versus stack thicknesses and indexes variations, through experiments, is also described. The final result is the complete characterization of the materials for thickness and scatterometry CD control, for photo feedback and for etch feed-forward deployment in an industrial mode. The analysis shows that scatterometry measurements on a standard 65 nm gate process ensure a better effectiveness than the CD Scanning Electron Microscopy (SEM) ones when injected in the Advanced Process Control (APC) system from photo to etch.

Property tables are not suitable for sensitivity and optimsation analyeses of complex thermodynamic systems that require the thermdynamic properties to be repetitively determinated at a large number of points in the system. This paper shows how Microsoft Excel can be used to develop computerised tables that automates the evaluation of thermodynamic properties. The property tables were copied in Excel and Visual Basic for Applications (VBA) was used to develop a number of user-defined functions that automate the interpolation of thermodynamic data. While maintaining the use of thermodynamic tables, which is an important educational process, the computerised tables can be a useful teaching aid that helps students to have better understanding of thermodynamics. The usefulness of the computerised tables is demonstrated by analysing the gas-turbine cycle with intercooling reheating and regeneration. Using the exact variable-specific heat method, the cycle was analysed for the effects of the inter-stage pressures on the plant's thermal efficiency and net specific-work. The paper shows that more accurate estimates of the optimum values of these pressures could be obtained than usually given by the widely adopted approximate constant-specific heat method. The Excel-based tables are particularly useful in developing countries like Sudan, where dedicated educational software is lacking.

Control loop performance assessment procedures are established as key cornerstone of process optimization and monitoring. Unfortunately, many methods do not consider the maximum possible performance which can be achieved by the fixed structure controller installed in the loop (typically PID). In authors' recent work such method was described. It was shown that -with respect to classical minimum variance method -the maximum performance is strongly influenced by the process normalized dead time. Consequently, the process model should be re-estimated during the plant operation. This paper describes technique which integrates continuous estimation of process model including normalized dead-time and control loop performance assessment. The process model is considered as fractional in order to fit well to distributed parameter systems appearing in chemical process control industries. All procedures are packed into advanced function blocks which are ready for real-time applications.

The increasing shift towards sustainable and net-zero targets has heightened interest in substituting hydrogen for natural gas in gas turbines and combined cycle power plants. This study investigates the compressibility of hydrogen within gas compressors, situated upstream of gas turbines, particularly when blended with various gases. Emphasis was placed on the inherent properties of hydrogen, including its behavior under compression, susceptibility to material embrittlement, and the influence of its gas characteristics on compressor performance. An extensive examination of prevalent compression methods, notably centrifugal compressors, was conducted to evaluate their efficacy in managing hydrogen at varying blend ratios. Issues related to material compatibility and safety were highlighted, alongside the formulation of reliable compression processes crucial for hydrogen-rich gas mixtures. Operational challenges posed by different hydrogen fuel proportions were identified, with proposed solutions including the implementation of precision control systems or the introduction of innovative materials. The study culminates in a discussion on prospective research directions and necessary technologies for effective hydrogen-rich gasification compression technology. The findings offer critical insights for ongoing initiatives aimed at enhancing and promoting hydrogen compression technology, facilitating the integration of hydrogen into existing infrastructures and supporting the sustainable development of the energy sector.

To determine the effect of intake air temperature on emissions of a diesel engine, a study consists of intake air at variable temperature. The Experiment was conducted on direct injection fuel, four cylinders, Experimental engine connected to gas analyzer. The experimental results show that there was a significant relation of the intake air temperature with performance of the engine. Increasing the intake air temperature HC, CO, NOX and CO2, are increase. Impact of intake air is noticeable when temperature of air is high such as 77ºc.

The product quality in semiconductor manufacturing is ensured with 100% inspection at each process step; hence inspection tools quickly run out of capacities resulting in the production cycle delays. To best utilize the production and inspection capacities, existing sampling (static, dynamic and smart) strategies are based on the risk and delays. These strategies, however do not guarantee a reliable lot sample that represents a likely yield loss and there is a high risk of moving a bad production lot to next production steps. We present a 3-step yield aware sampling strategy to optimize inspection capacities based on the likely yield loss with the predictive state (PSM) and alarm (PAM) models as: (i) classify potentially suspected lots, (ii) cluster and/or populate suspected lots in the priority queues and (iii) apply last in first out (LIFO) to optimize capacities. This strategy is implemented with two heuristics. We also present a data model with ASCM (Alarm and State Control Management) tool for the multi source data extraction, alignment and pre-processing to support the validation of [PSM, PAM] predictive models.

Intel factories are being challenged by an increasingly diverse product and technology mix. Semiconductor manufacturers have successfully leveraged advanced process control (APC) for overlay and critical dimension (CD) in the lithography module. Trends are continuously corrected through in-line metrology feedback, keeping the process on target. Applying an APC system suitable for litho control in high-volume manufacturing has resulted in significant improvement in registration capability and potential for better exposure focus and CD control. Index Terms-Advanced process control (APC), critical dimension (CD), exposure focus CD control, overlay, run-to-run control, scanner registration control.

Combustion, the process of burning, is defined as a chemical reaction between a combustible reactant (the fuel) and an oxidizing agent (such as air) in order to produce heat and in most cases light while new chemical species (e.g., flue gas components) are formed. This book covers a gap on the market by providing a concise introduction to combustion. Most of the other books currently available are targeted towards the experienced users and contain too many details and/or contain knowledge at a fairly high level. This book provides a brief and clear overview of the combustion basics, suitable for beginners and then focuses on practical aspects, rather than theory, illustrated by a number of industrial applications as examples. The content is aimed to provide a general understanding of the various concepts, techniques and equipment for students at all level as well as practitioners with little or no prior experience in the field. The authors are all international experts in the field of combustion technology and adopt here a clear didactic style with many practical examples to cover the most common solid, liquid and gaseous fuels. The associated environmental impacts are also discussed so that readers can develop an understanding of the major issues and the options available for more sustainable combustion processes.

Realistic kinetic modeling of fluid catalytic cracking (FCC) units requires detailed composition of the feed stream in terms of paraffins, naphthenes and aromatics(PNA) which cannot be analyzed in a field laboratory. This paper presents an artificial neural network (ANN) model to predict detailed composition of FCC feed using routinely measured properties such as density, ASTM distillation temperatures, Conradson carbon residue (CCR) content, sulfur and total nitrogen as inputs to the model. Several feedforward-error back propagation networks with different number of neurons in hidden layers were studied using Levenberg-Marquardt (LM) training algorithm. Among different network architectures investigated, the ANN model with 8 inputs, namely density and ASTM distillation temperatures except IBP, FBP and only one neuron in the output layer to predict paraffin, naphthene and aromatic contents individually showed the best agreement with the experimental results within permissible limit. These compositions when used with a 10-lump kinetic model of FCC unit, successfully simulated plant performance for several different feeds.

Zusammenfassung Im Projekt ReCo (Rendezvous Control) wird die automatisierte Synchronisation und Landung eines unbemannten Flugsystems auf einem Bodenlandesystem untersucht. Der Einsatz eines solchen Landesystems ermöglicht einen luftfahrzeugseitigen Verzicht auf ein komplexes und schweres Fahrwerk. Die daraus resultierende Massenreduktion ist im Hinblick auf den Einsatz von unbemannten "High Altitude Long Endurance" (HALE) Luftfahrzeugen von besonderer Bedeutung, da so deren Flugleistung und Nutzlast optimiert werden können. Auch bei Großraumflugzeugen ergibt sich durch Dominoeffekte auf die Struktur-und Triebwertksauslegung eine signifikante Massenreduktion. Dies führt zu einem effizienteren und umweltfreundlicheren Betrieb. In diesem Beitrag wird für die Präzisionslandung auf einem bewegten Bodensystem eine bestehende Flugzustandsregelung um einen modellprädiktiven Ansatz erweitert. Dieser basiert auf einem linearen Modell der Seitendynamik eines unbemannten, skalierten Beispielflugzeugs unter Berücksichtigung atmosphärischer Störungen. Diese werden in den relevanten Landephasen am Bodensystem gemessen. Die Simulationsergebnisse zeigen, dass der untersuchte, modellprädiktive Regelansatz für Präzisionslandungen geeignet ist und die Regelgüte durch eine Berücksichtigung der prädizierten dominaten Störgröße, dem instationären Wind, verbessert werden kann. Abschließend wird eine Abschätzung der nötigen Güte der Prädiktion der Störung diskutiert.