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Higher quality primary aluminum is increasingly required for high-performance alloy products. The influence of the impurity contents in such raw materials as prebaked anodes, alumina, etc. and impact of the process operations on the quality of primary aluminum are discussed in detail in this paper. The results show that Fe and Si are the major impurity components migrated in primary aluminum. The impurity Fe mainly comes from the cell covering material, prebaked anode, alumina, and operation process. The impurity Si mainly comes from the same sources as well as fluoride salts. Besides, a kind of anti-oxidized prebaked anodes with excellent reactivity to CO2 and air was studied and developed to reduce obviously Si content in primary aluminum and to improve aluminum quality.

Real-time optimization (RTO) is a steady-state model-based method used for optimizing process operation in chemical plants. The most common implementation, two-step RTO (TS-RTO), updates the steady-state model parameters in the first step, and optimizes this model in the second step. It has a major drawback, which is the need to wait for steady-state. If data from transient periods is directly used for updating the steady-state model parameters, the production optimization results will most likely be sub-optimal, decreasing the benefits. This becomes even more acute if the system is constantly affected by disturbances and has long settling times. Matias and Le Roux [2018] proposed a TS-RTO variant that uses a dynamic estimator to update the steady-state model parameters, which was named real-time optimization with persistent parameter adaptation (ROPA). By using dynamic estimation, it ensures that the model is always updated to the plant and the steady-state optimization can be scheduled at any desired rate without needing to wait for steady-state. This hybrid approach has been successfully tested in simulations. In this paper, we show its first implementation in a lab-scale rig, which emulates a subsea oil well network. The results show that the hybrid approach enables an increase in the optimization frequency and a decrease in the optimization results variability, improving the overall economic performance when compared to the TS-RTO implementation.

Objectives:In this study, through the results of the high-concentration 2-MIB (2-Methyl Isoborneol) treatment by two different types of advanced treatment plants (Post Peroxone+GAC, UV/H2O2+GAC F/A) which intake raw water from the same water intake facility, the 2-MIB removal characteristics by oxidation process of each WTPs (Water Treatment Plants) were compared and analyzed, and optimal operation methods were derived.Methods:The 2-MIB removal rate was compared and analyzed according to each AOP (Advanced Oxidation Process) operating conditions (Post Peroxone+GAC of the G WTP and UV/H2O2+GAC F/A of the I WTP). The optimal equations of chemical injection were derived through the correlation between the operating conditions of the AOP for each WTPs and 2-MIB removal rate. By analyzing the operating characteristics of each WTPs, the cost and unit price for optimal operation were calculated according to the 2-MIB concentration of raw water and water production. Optimal operating conditions were derived through the performance of oxidation facilities and chemical injection equations of each WTPs, and economical operating plans were reviewed through linked operation of 2 WTPs.Results and Discussion:The 2-MIB removal rates for each WTPs were 70~100% for the G WTP and 50~96% for the I WTP. The operating conditions affecting the 2-MIB removal were [O3 injection×contact time], H2O2/O3 for Post Peroxone of the G WTP, and [UV dose×H2O2 injection] for UV/H2O2 of the I WTP. As a result of comparing the operating cost(electric power cost + chemical cost) of each WTPs, I WTP was 6.6~24.3 KRW/m3 higher than G WTP. It is considered to be because the H2O2 injection was 11~43 times for UV/H2O2 than Post Peroxone. Optimal operating conditions could be derived through the performance evaluation of each oxidation facilities and chemical injection equations of each WTPs. The G WTP and the I WTP are equipped with pipe line for linked operation in the water supply pipes, so the water production for each WTPs can be distributed. In the case of the same water production, it was confirmed that the unit price can be reduced when the water production ratio of the G WTP is increased. Because the decrease in cost of the I WTP is higher than the increase in cost of the G WTP.Conclusions:It was confirmed that both Post Peroxone+GAC of G WTP and UV/H2O2+GAC F/A of I WTP were effective in 2-MIB treatment. As for the operating cost, it was analyzed that UV/H2O2 had higher unit pice than Post Peroxone because of the large amount of H2O2 injection. Considering the 2-MIB removal rate and operating cost of each WTPs, it was possible to derive the optimal operating conditions for each WTPs and a linked operation plan.

Turbidity is one of the key water quality parameters in environmental monitoring, water treatment or industrial process operation. Turbidity is however very challenging to measure reliably due to the many factors affecting the reading and functionality of the measurement device. In this paper, the results of the experiments to study the effects of changes in water quality and environmental condition to the readings of turbidity measurement devices are presented. The experiments were carried out in stable laboratory conditions where controlled changes were made to water pH, the temperature, salinity, colour, environment brightness and the mixing speed of the water. The study showed that even though the actual turbidity of the water remained constant the changes in other variables caused in the worst-case significant disturbances to the turbidity measurements. This knowledge is vital, for instance in monitoring or developing a robust model for forecasting regional turbidity in natural waters.

In modern industrial processes, timely detection and diagnosis of process abnormalities are critical for monitoring process operations. Various fault detection and diagnosis (FDD) methods have been proposed and implemented, the performance of which, however, could be drastically influenced by the common presence of incomplete or missing data in real industrial scenarios. This paper presents a new FDD approach based on an incomplete data imputation technique for process fault recognition. It employs the modified stacked autoencoder, a deep learning structure, in the phase of incomplete data treatment, and classifies data representations rather than the imputed complete data in the phase of fault identification. A benchmark process, the Tennessee Eastman process, is employed to illustrate the effectiveness and applicability of the proposed method.

With the emergence of Industry 4.0 and Big Data initiatives there is a renewed interest in leveraging the vast amounts of data collected in (bio)chemical processes to improve their operations. The objective of this manuscript is to provide a perspective of the current status of Big Data-based process control methodologies and the most effective path to further embed these methodologies in the control of (bio)chemical processes. Therefore, this manuscript provides an overview of operational requirements, the availability and the nature of data, and the role of the control structure hierarchy in (bio)chemical processes and how they constrain this endeavor. The current state of the seemingly competing methodologies of Statistical Process Monitoring and (Engineering) Process Control is examined together with hybrid methodologies that are attempting to combine tools and techniques that belong to either camp. The technical and economic considerations of a deeper integration between the two approaches is then explored and a path forward is proposed.

Recurrent neural networks (RNNs) have been predominately employed to deal with industrial process operation modeling problems that are hard to be described by first-principles approaches. However, ...

Nowadays, optimizing the efficiency of energy and production has become a hot research area. In this paper, a hybrid multiobjective optimization model integrating NSGA-II and the genetic algorithm ...

Process optimization is an important topic in process industry, most process industry optimization works are based on mechanism models or performance test methods. However, it is very difficult to carry out optimization in actual operation because of the difficulty in obtaining the mechanism model, the difficulty in on-line measurement of objective function and the high test cost. In order to solve the problem, an online optimization method based on system identification is proposed. By replacing the unmeasurable variable with the measurable variable, the process model is identified on-line, and the gain of identified model is used as the optimization gradient to find the optimal variable value on-line. The method is verified using both simulation and real plant data.

Muscle proteins play an essential role in the physical stability and sensory perception of processed meat products. The specific functional behavior of proteins is influenced by their physicochemical properties (intrinsic) as well as processing parameters (extrinsic). Of all muscle constituents, myofibrillar proteins, especially myosin or actomyosin, are the most important functional components that contribute to the gelling, emulsifying, and water-binding characteristics of cooked products. A clear understanding of the protein structure–functionality relationship is crucial for the optimization of product formulation, process operations, and quality control.

In this paper, we present both academic and industrial perspectives on the research and applications of Process Systems Engineering (PSE). After a brief introduction on the history of PSE, we describe the major research accomplishments in the areas of process simulation, conceptual design and synthesis, process control, process operations and optimization. This is followed by a discussion on the industrial impact and benefits of this work, which have made it to be industrially relevant. Next, we address the issue of the current standing of PSE both in academia and in industry, and for which we present results of a survey conducted by the authors. Finally, we close with a discussion on future challenges in PSE from both the industrial and academic perspectives.

Background:Bridging the gap between different phenomena, mechanisms and levels of description, different design methods can converge in a unitary way of formulation. This protocol consolidates the analogy and parallelism in the description of any unit operation of separation, as is the particular case of sedimentation. This holistic framework is compatible and complementary with other methodologies handled at length, and tries to contribute to the integration of some imaginative and useful - but marginal, heuristic or rustic- procedures for the design of settlers and thickeners, within well founded and unified methodology.Objective:Classical models for hindered sedimentation allow solid flux in the direction of the gravity field to be formulated by analogy to changes obeying a potential, such as molecular transfer in the direction of the gradient and chemical transformation throughout the reaction coordinate. This article justifies the fundamentals of such a suggestive generalized analogy through the definition of the time of the sedimentation unit (TSU), the effective surface area of a sedimentation unit (ASU) and the number of sedimentation units (NSU), as elements of a sizing equation.Methods:This article also introduces the generalization of the model ab initio: Analogy is a well known and efficient tool, not only in the interpretation of events with academic or coaching purposes, but also in the generalized modelling, prospective, innovation, analysis and synthesis of technological processes. Chemical Engineering protocols for the basic dimensioning of Unit Operations driven by potentials (momentum, heat and mass transfer chemical reaction) are founded in macroscopic balances of mass and energy.Results:These balances, emphatically called “design equations”, result from the integration of mechanistic differential formulations at the microscopic level of description (“equations of variation”). In its turn, these equations include phenomenological terms that may be formulated in corpuscular terms in the field of Chemical Physics. The design equation correlates requirements in equipment (e.g. any practical forms of size and residence or elapsed time for an efficient interaction) to the objectives of the operation (e.g. variations in mass or energy contents of a confined or fluent system). This formulation allows the identification of different contributions: intrinsic terms (related to mechanistic kinetics of the phenomena) and circumstantial terms (related to conditions and variables of operation).Conclusion:In fact, this model suggests that temporal or spatial dimensions of the equipment may be assumed to depend irrespectively on two design contributions: the entity of a representative “unit of operation (or process)” - illustrated by a descriptor of this dimension- and the “number of (these) units” needed to achieve the separating or transformative objectives of the operation.

The distillation column control strategy in a standalone column ensures a safe and stable column operation by holding the purities/or impurities in distillate and bottoms product streams. In general, the sensitivity analysis method provides the column control variable which contributes in robust process operation, however, in this example the importance of control variables selection through different methods are compared for a specific case study where the distillation column is located/placed inside the recycle loops of two different recycle streams. The control variables for distillation column located within two different recycle loops is first selected using sensitivity analysis shows an integrating (ramp like) recycle flow rate for a nominal (±10%) throughput change. Later, a thorough dynamic analysis is implemented with control variables obtained from different methods to understand this peculiar recycle response. Further, these methods are discussed to improvise the dynamic response. Keywords: Azeotropic distillation, Control variable, Material imbalances, Recycle response

Industrial alarm systems play an essential role for the safe management of process operations. With the increase in automation and instrumentation of modern process plants, the number of alarms that the operators manage has also increased significantly. The operators are expected to make critical decisions in the presence of flooding alarms, poorly configured and maintained alarms and many nuisance alarms. In this environment, if the incoming alarms can be correctly predicted before they actually occur, the operators may have a chance to address and possibly avoid abnormal behaviors by taking corrective actions in time. Inspired by the application of deep learning in natural language processing, this paper presents an alarm prediction method based on word embedding and recurrent neural networks to predict the next alarm in a process setting. This represents both a novel approach to alarm management as well as a novel application of natural language processing and deep learning techniques to this problem. The proposed method is applied to an actual case study to demonstrate its performance.

The production of commercially valuable biotherapeutic molecules in mammalian systems has expanded significantly in the last thirty years, but growing economic pressures within the industry are driving efforts to reduce costs and enhance process yields. At the upstream stage, two complementary approaches have evolved to increase productivity and maintain consistent product quality, that is either by altering the cell directly or by manipulating its environment. This review focuses on novel approaches to impact productivity and product quality by manipulating the environment through: (a) altering media composition; (b) modulating operating conditions such as pH and temperature; or (c) intensifying process operations by switching from fed-batch to continuous processes.

Process-aware attack detection plays a key role in securing cyber-physical systems. A process-aware detection system (PADS) identifies a baseline behaviour of the physical process in cyber-physical systems and continuously attempts to detect deviations from the baseline attributed to malicious modifications in the process operation. Typically, a PADS triggers an alarm whenever the detection score crosses a fixed and predetermined threshold. In this paper, we argue that in the context of securing cyber-physical systems, relying on a single fixed threshold can undermine the effectiveness of the PADS, and propose a context-aware framework for determining two-dimensional thresholds that enhance the sensibility and reliability of such detection systems by rendering them more robust to false detection. In addition, we propose an algorithm, out of many possible, within this framework as a practical example.

The Eight Disciplines (abbreviated as 8D) of Problem-Solving is a problem-solving model intended to find the root cause (RC) of a problem, formulate a short-term fix, and implement a long-term corrective and preventive action (CAPA) solution to prevent recurring issues. The 8D model is a key tool for continuous process improvement. Therefore, this document is prepared to properly implement the 8D problem-solving model for company process operations, including the suppliers and internal operations, procedures, and processes.

Developing efficient anomaly detection and isolation schemes that offer early detection of potential anomalies in the monitored process and identify and isolate the source of the detected anomalies is indispensable to monitor process operations in an efficient manner. This will further enhance availability, operation reliability, and profitability of monitored processes and reduce manpower costs. This book is mainly devoted to data-driven fault detection and isolation methods based on multivariate statistical monitoring techniques and deep learning methods. In this chapter, conclusions and further research directions are drawn.

In this work, knee prosthesis manufacturing process using CO2 cooling is described. It is necessary to use 5 continuous axis milling in order to manufacture this complex geometry. However, surfaces complexity and material (titanium) low machinability characteristics present a challenge for manufacturers. In this case, process operations and machining parameters for this component manufacturing are specified. Besides, virtual verification of machining process is also performed in order to avoid collisions. Moreover, cooling is a key factor in this case regarding medical components cleaning specifications. Traditional coolants are replaced by CO2 cooling.; Honako lan honetan belaun-protesiko osagarrien fabrikazioa azaltzen da CO2 hozgarri kriogenikoa erabiliz. Osagarrien konplexutasuna dela eta, beharrezkoa da 5-ardatzen interpolazioa gauzatzea fresaketa-eragiketetan. Gainazalen konplexutasun horrek eta materialaren (titanioaren) mekanizagarritasun baxuak mekanizazio-erronka berriak sortzen dituzte. Kasu honetan, osagarrien fabrikaziorako erabilitako mekanizazio-estrategiak eta mekanizazio-aldagaiak deskribatzen dira. Gainera, mekanizazioan ager daitezkeen talkak saihesteko, egiaztatze birtuala erabili da. Horrez gain, hozketa faktore garrantzitsu bat da osagai medikoen garbitasuneko beharrizanei dagokienez. Ohiko hozte-teknikak CO2 hozgarri kriogenikoarekin ordeztu dira.

With liquefied natural gas becoming increasingly prevalent as a flexible source of energy, the design and optimization of industrial refrigeration cycles becomes even more important. In this article, we propose an integrated surrogate modeling and optimization framework to model and optimize the complex CryoMan Cascade refrigeration cycle. Dimensionality reduction techniques are used to reduce the large number of process decision variables which are subsequently supplied to an array of Gaussian processes, modeling both the process objective as well as feasibility constraints. Through iterative resampling of the rigorous model, this data‐driven surrogate is continually refined and subsequently optimized. This approach was not only able to improve on the results of directly optimizing the process flow sheet but also located the set of optimal operating conditions in only 2 h as opposed to the original 3 weeks, facilitating its use in the operational optimization and enhanced process design of large‐scale industrial chemical systems.

Monoclonal antibodies (mAbs) represent the largest group of biopharmaceuticals used for human therapy, diagnostics, and imaging. Full-length mAbs with human-like posttranslational modifications are almost exclusively expressed in mammalian cell cultures. Progress in genetic engineering techniques and process development enables the development of optimized cell lines and manufacturing processes. The main factors that influence the yield of a production process are the time to accumulate a target amount of biomass, the process duration, and the specific productivity of the employed cell line. In this chapter, the contribution of the cell line, culture medium, cultivation parameters, and mode of process operation to product yield and quality are discussed.

This paper presents a long range process fault prognosis system using long short-term memory (LSTM) network. Data from historical process operation with faults present are used to train LSTM networks. During process monitoring, a principal component analysis (PCA) model developed from normal historical process operation data is used to detect the presence of a fault. Once a fault is detected, reconstruction based fault diagnosis is used to diagnosis the detected fault. Then the trained LSTM network corresponding the diagnosed fault is use to provide long range fault magnitude forecast. The proposed method is applied to a simulated continuous stirred tank reactor (CSTR) and is compared with fault prognosis using extreme learning machine (ELM). The results show that the proposed fault prognosis method based on LSTM network can achieve excellent long range prognosis performance.

Alarm floods are considered to be the major obstacles that prevent the smooth process operations of large-scale industrial facilities. During an alarm flood situation, industrial operators often get confused by too many alarms and thus have difficulties in observing and handling critical alarms. In recent years, sequence alignment based similarity analysis has emerged as an effective way to handle alarm floods. Alarm floods caused by the same fault are very likely to consist of the same group of alarms in a certain sequential order. Conducting realtime sequence alignment of industrial alarm floods can help operators to quickly recall the root cause and make prompt corrective actions. This paper proposes the online similarity analysis of alarm floods based on the Smith-Waterman and Needleman-Wunsch algorithms, and compares their differences and application conditions. Case studies are provided to illustrate the proposed online similarity analysis methods and the differences of the two sequence alignment algorithms.

This chapter presents the motivation for developing fault diagnosis application in industrial systems. Fault diagnosis methods can be broadly categorized into model-based and data-driven. Model-based strategies are briefly discussed while highlighting the increasing tendency to the use of data-driven methods given the increasing data available from process operations. The classic data-driven fault diagnosis loop is presented and each task is described in detail. A procedure is presented for the systematic design of data driven fault diagnosis methods. Finally, the fault diagnosis problem for multimode processes is briefly discussed.

Drinking water treatment processes are capable of removing microcystins but consistent operation of processes optimized for cyanobacterial harmful algal bloom (cHAB) conditions is not fiscally feasible. Therefore, utilities must ready themselves and start the cHAB processes as a reactionary response. Predictive analytics and modelling are impactful tools to prepare water systems for cHABs, but are still in early stages of development. Until those prospective models are completed, a method to determine best actions in advance of a bloom event thus improving system resiliency is needed. In this study, an adaptation of the quantitative microbial risk analysis (QMRA) methodology was applied to develop this method. This method and resulting models were developed around the Toledo (Ohio, USA) water crisis of 2014, but being mechanistic, they are easily adaptable to other systems' process operations data. Results from this internally validated model demonstrate how rapid action using both powdered activated carbon and measured increases in chlorine dose can mitigate health risks. Our research also demonstrates the importance of modelling the cellular status of the toxins (toxins either in an intact cell or in the water from a lysed cell). Risks were characterized using hazard quotients (HQ) and at the peak of the crisis ranged from a minimum of 0.00244 to a maximum of 2.84 for adults. In simulations where cHAB-specific treatment was used this decreased to 0.00057 and 0.236 respectively. We further outline how this methodology can be used to simulate water system resiliency to likely and aberrant microbial hazard events to plan for the best interventions to protect public health. This method can be used for other hazards expected to be variable in the future, where system prepatory planning is critical to continued public health protection. Considering the water quantity and quality fluctuations occurring and likely to intensify under climate change, this type of computationally supported preparedness is vital to maintaining robust water system resiliency.

The early development of the technology of local exhaust began around 1928 with academic attention being paid to the air patterns developed by hoods intended to capture dust particles. The environment within which a local exhaust system is employed can affect its efficiency to a considerable degree. Exhaust hoods used in local exhaust systems are in close proximity to the process operation. Multisource systems are more common and have the complication of seeing that the proper air is exhausted from each exhaust hood. Exhaust air controls the movement of air into the chamber but in a pattern that does not cross the falling material, thereby avoiding removal of excessive production material. The chapter focuses on the exhaust hood, the design of which is the key to successful engineered local exhaust. Good duct design includes the use of wide radius elbows; flanged and sealed connections; and grounding to avoid the build-up of static electricity charges.

Coating process is a critical unit operation for manufacturing solid oral dosage forms. For a long time, the coating weight gain has been discerned as the most important, if not only, characteristic describing the coating quality. As the introduction of quality by design (QbD) and advancement of process analytical technology (PAT), nowadays more techniques are available to analyze other quality attributes which have been overlooked but have substantial impacts on the performance of coated products. The techniques that permit rapid and non-destructive measurements are of particular importance to improve process operation and product quality. This article reviews the analytical techniques that have been and potentially could be used as PAT tools for characterizing the quality of pharmaceutical coating product. By identifying the challenges and pitfalls encountered during PAT application, the review aims at fostering the adoption of PAT for paving the way to enhanced quality and efficiency of the coating processes.

In the era of intensive industry and agriculture development, pollution of water reservoirs is becoming an increasingly important problem. This review focuses on remediation as a tool for removal of waterborne pathogens such as bacteria, viruses, protozoan parasites, and microbial toxins which can cause the outbreak of many diseases. In the case of natural water reservoirs, the most frequent cause of deterioration of the physicochemical and sanitary-hygienic parameters of water is the excess of biogenic compounds which leads to eutrophication. Classic methods of water reservoir reclamation are either uneconomical or inefficient or their positive effects are visible only after many years. Therefore, the right solution is to assess the ability of treatment processes to achieve health-based water safety targets and to identify control measures in the process operation. It should be emphasized that during the removal of pathogens, the following aspects are very important: monitoring the changes in the metabolism of organic and inorganic contaminants and observing biodiversity of the polluted environment.

Smart manufacturing should improve the economy of existing manufacturing processes, which may require operating the processes more “intensely.” Due to the limitations of existing (generally univariate) functional safety systems, this more intense operation may not be realistic without innovation in functional safety. Model-predictive safety (MPS), introduced in 2016, represents an evolution in functional safety. Unlike conventional functional safety systems, MPS generates alarm signals that are predictive and systematically accounts for process nonlinearities and variable interactions. In real time, it detects potential and imminent future process operation hazards, and prescribes optimal preventive and mitigating actions proactively. This chapter presents the concept and the main components of MPS, i.e., a state-estimate predictor, process operation constraints, and min-max optimization problems. Offline solutions of the min-max optimization problems are the optimal actions that online MPS uses to detect operation hazards and prescribes. As a case study, MPS is applied to a polymerization reactor example.

Traditionally, planning, scheduling and optimal control problems are solved in a decoupled way, neglecting their strong interdependence. Integrated Planning, Scheduling and optimal Control (iPSC) aims to address this issue. In this chapter, current developments on the topic of integrating control with process operations are reviewed and a new approach for the iPSC of continuous processes aiming to reduce model and computational complexity is proposed. The resulting problem is a mixed integer program for which different solution strategies are employed and analysed.

Pressure-driven membrane processes, nanofiltration (NF), and reverse osmosis (RO) are favorable advanced treatment processes for micropollutant removal due to their high micropollutant rejections. An array of complex mechanisms under the influence of process operating conditions involving interactions between micropollutants, water matrix, and membrane contribute to the overall removal of micropollutants in NF and RO processes. The extensive research in this area has contributed to knowledge advancement that has aided process optimization and development of new NF and RO membranes. However, most research is conducted in lab-scale studies, which may not accurately represent the micropollutant rejection behavior and performance at full-scale operation. Long-term full-scale NF and RO systems assessment would be useful to evaluate the fluctuations in feed and operation variables, and to establish the dataset for more accurate model prediction. Continuous effort in systematic research is required to fill the knowledge gap of this dynamic research topic as the list of micropollutants continues to expand with the development of new membrane materials and process operations. Further challenges in the treatment and management of NF and RO concentrates would need to be addressed to provide an overall barrier and to safeguard the environment from potential adverse impacts of micropollutants.

The characterization of immobilized enzymes allows the evaluation of the immobilization process itself and also the projection of the immobilized enzyme performance under process operation conditions. Based on such characterization, strategies for support functionalization and enzyme immobilization into the activated support can be selected, determining the best conditions for conducting such steps in view of the intended use of the biocatalyst, establishing a linkage between biocatalyst production and biocatalyst use. The determination of the catalytic potential of the immobilized enzyme under operational conditions is a priceless parameter that takes into account both activity and stability, including the effect of both mass transfer limitations (diffusional restrictions) and intrinsic enzyme inactivation upon the immobilization process.

The use of immobilized enzymes in industry is becoming a routine process for the manufacture of many key compounds in the pharmaceutical, chemical, and food industry. Some enzymes like lipases are naturally robust and efficient, can be used for the production of many different molecules, and have found broad industrial applications. Some more specific enzymes, like transaminases, have required protein engineering to become suitable for applications in industrial manufacture. For all enzymes, the possibility to be immobilized and used in a heterogeneous form brings important industrial and environmental advantages such as simplified downstream processing or continuous process operations. Here, we present a series of large-scale applications of immobilized enzymes with benefits for the food, chemical, pharmaceutical, cosmetics, and medical device industries, some of them hardly reported before.

Desulfurization, known as the removal of sulfur from oil, is extremely important in the petroleum processing industry and in the environmental protection. Several oil-upgrading processes such as desulfurization and catalysts such as alumina loaded with molybdenum have been proposed to deal with the problem of removing sulfur-containing compounds from light oil. Thus, several parameters are required to be experimentally optimized which demands a lot of work including reagents. Advanced mathematical tools can be used to optimize the desulfurization process and to study the related factors. The modeling and simulation of the desulfurization process have been proposed in several studies in order to facilitate a better understanding of the process operations. Machine Learning (ML) is regarded as a promising methodological area to perform such optimization and analysis. This review describes the relevant methods for dealing with the applications of ML for desulfurization in oil. Although a good number of research papers have appeared in recent years, the application of ML for desulfurization is still a promising area of research. The review presents an overview of the ML methods and their categories in desulfurization. It discusses and compares the methods that employ ML to optimize the desulfurization process. The review also highlights the findings and possible research directions.

Principal Component Analysis (PCA) is the most common Multivariate Statistical Process Control (MSPC) method that is widely used for Fault Detection and Diagnosis (FDD). Since early abnormality detection with high accuracy is required for safe and reliable process operation, False Alarms Rate (FAR), Missed Detection Rate (MDR) and the detection time delay are the major factors that must be taken into consideration when developing any process monitoring scheme. Unfortunately, the PCA performance, with fixed limits, is weak in terms of the stated factors. In contrast, conventional Moving Window PCA (MWPCA) is an adaptive technique which updates both the PCA model and the thresholds once a new normal observation is available. Yet, MWPCA methodology still does not reduce the MDR and the detection delay. In this paper, a Modified MWPCA (MMWPCA) with Fuzzy Logic Filter (FLF) is proposed to enhance the monitoring performance of PCA. It is an adaptive approach with a fixed model that combines both aforementioned techniques. The aim of using FLF is to ensure robustness to false alarms without affecting the Fault Detection (FD) performance. The application of the proposed method has been carried out on the Tennessee Eastman Process (TEP). Hold-one and hold-five MMWPCA with FLF are applied and compared to recent FDD work in the literature. The obtained results demonstrate the superiority of the proposed technique in detecting different types of faults with high accuracy and with shorter time delay.

Most existing risk management models for process industries do not consider the effect of insurance coverage, which results in an overestimation of overall risk. A model is presented in this article to study the effect of insurance coverage of health, safety, environmental, and business risks. The effect of insurance recovery is modeled through the application of adjustment factors by considering the stochastic factors affecting insurance recovery. The insurance contract's conditions, deductibles, and policy limits are considered in developing the insurance recovery adjustment factors. Copula functions and Monte Carlo simulations are used to develop the distribution of the aggregate loss by considering the dependence among loss classes. A case study is used to demonstrate both the practical application of the proposed insurance model to improve management decisions, and the mitigating effect of insurance to minimize the residual risk.

Mobile Ad-Hoc Network (MANET) consists of a collection of highly mobile nodes and stationary system and components which interact with each other closely and communicate to achieve the required network functionality. Since MANET is a distributed network and has no centralized controlling unit to handle the overall network operation, it is necessary to ensure that all the operations are performed in a synchronised manner. This is due to the fact that in MANET, several process share the network resource and if two or more process try to access a shared resource at the same time instant, then it may result in distributed mutual exclusion issue. To overcome this issue, it is necessary to ensure that all the processes work in synchronization with the other. In this paper, we propose to develop a Priority based Hybrid Mutual Exclusion Algorithm with Starvation Avoidance for MANET. In this technique, the process operations which use the shared resource are prioritized in an efficient manner to allow usage of the shared resource by one process at a time, and also by avoiding the starvation issue usually seen in MANET.

Since the authorization of the Clean Air Act Amendments of 1990, the air quality in the USA has significantly improved because of strong public support. The lessons learned over the last 25 years are being shared with the policy analysts, technical professionals, and scientist who endeavor to improve air quality in their communities. This paper will review how the USA has achieved the “high” standard of air quality that was envisioned in the early 1990s. This document will describe SO(2) gas emission reduction technology and highlight operation of emission monitoring technology. This paper describes the basic process operation of an air pollution control scrubber. A technical review of measures required to operate and maintain a large-scale pollution control system will be described. Also, the author explains how quality assurance procedures in performance of continuous emission monitoring plays a significant role in reducing air pollution.

To mitigate climate change, the reduction of anthropogenic CO2 emissions is of paramount importance. CO2 capture and storage has been identified as a promising short- to midterm solution, yet the underground storage of CO2 faces often severe public resistance. In this regard, the conversion of the CO2 captured into useful chemicals or fuels is an attractive alternative. Here, we propose and experimentally demonstrate a process that directly integrates CO2 utilization into CO2 capture allowing for the full conversion of the CO2 captured and the selective production of a synthesis gas. The process is attractive both economically and from a process operation point of view as the coupled reactions are performed in a single reactor. The concentration of (unreacted) CO2 in the off-gas is below 0.08%, demonstrating the almost full conversion of the CO2 captured in a single, integrated step. Importantly, the process is demonstrated using a nonprecious metal catalyst and an inexpensive naturally occurring CO2 sorb...

In this work, the control structure design for a continuous extractive distillation scheme using entrainers for separating THF–water azeotropic mixture into high purity product (THF) has been studied. The selection of suitable entrainer for the undertaken process was purely based on economical design criteria where total annualised cost (TAC) for different entrainers were first calculated and further compared. Once the suitable entrainer was selected based on the design economics, later the plantwide control structure was evaluated for disturbance rejection capabilities and smooth process operations in the face of feed throughput and composition change. For economical design operations, the recycle rate of entrainers has to be kept around a minimum recycle rate. However, operating below minimum entrainer recycle rate leads the product purity to fall sharply in conventional control structure designs due to THF–Water azeotropic constraints. The presented control structure design in this works ensures that product purity is not lost even in case of sever disturbances in feed throughput (by ±10%) or a changed feed composition (by ±5%).

The aim of the current review is to analyze trends in development of an efficient technology for polyhydroxyalkanoate (PHA) biomanufacture highlighting the up-to-date progress on PHA biosynthesis and focusing on the downstream processing. Three main production pathways were identified: through microbial, enzymic, or plant routes. Microbial fermentation processes were predominant, with a wide range of microorganisms, starting materials and culture conditions reported. Largely, two schemes for recovering PHAs from the reaction medium post fermentation were identified: dissolving biomass to separate PHAs granules with strong oxidants, and extracting PHAs directly from the biomass using suitable solvents. For the valuable industrial scale biosynthesis of PHA several technological elements need to be applied such as robust whole-cell microbial catalyst with its optimal culturing conditions, suitable carbon source, proper mode of process operation, as well as economical and ecological purification methods.

In order to increase the strength of the adhesive bonding of rubber based on SKI-3 rubber substance to metal cords, the effect of adhesion-promoting agents, which are silicic filler modified by metal ions of variable valency (Co and Ni), has been investigated. It is shown that such adhesion-promoting agents do not have a significant effect on the technological and physicomechanical properties of rubber at room temperature, but increase the resistance of vulcanizates to elevated temperatures (up to 100°C) and increase the strength of the rubber–metal cord connection under the influence of aggressive factors in the process operation of metal cord products.