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Given the exponential rise in the amount of data requiring processing in all engineering fields, phenomenological models have become computationally cumbersome. For this reason, more efficient data-driven models have been recently used with the purpose of substantially reducing simulation computational times. However, especially in process engineering, the majority of the proposed surrogate models address steady-state problems, while poor studies refer to dynamic simulation modeling. For this reason, using a response function-based approach, a crystallization unit case study was set up in order to derive a dynamic data-driven model for crystal growth whose characteristic differential parameters are derived via Response Surface Methodology. In particular, multiple independent variables were considered, and a well-established sampling technique was exploited for sample generation. Then, different sample sizes were tested and compared in terms of accuracy indicators. Finally, the domain partition strategy was exploited in order to show its relevant impact on the final model accuracy. In conclusion, the outcome of this study proved that the proposed procedure is a suitable methodology for dynamic system metamodeling, as it shows good compliance and relevant improvement in terms of computational time. In terms of future research perspectives, testing the proposed procedure on different systems and in other research fields would allow for greater improvement and would, eventually, extend its validity. [ABSTRACT FROM AUTHOR]

Given the exponential rise in the amount of data requiring processing in all engineering fields, phenomenological models have become computationally cumbersome. For this reason, more efficient data-driven models have been recently used with the purpose of substantially reducing simulation computational times. However, especially in process engineering, the majority of the proposed surrogate models address steady-state problems, while poor studies refer to dynamic simulation modeling. For this reason, using a response function-based approach, a crystallization unit case study was set up in order to derive a dynamic data-driven model for crystal growth whose characteristic differential parameters are derived via Response Surface Methodology. In particular, multiple independent variables were considered, and a well-established sampling technique was exploited for sample generation. Then, different sample sizes were tested and compared in terms of accuracy indicators. Finally, the domain partition strategy was exploited in order to show its relevant impact on the final model accuracy. In conclusion, the outcome of this study proved that the proposed procedure is a suitable methodology for dynamic system metamodeling, as it shows good compliance and relevant improvement in terms of computational time. In terms of future research perspectives, testing the proposed procedure on different systems and in other research fields would allow for greater improvement and would, eventually, extend its validity.

In order to response to the “double carbon” strategy for reducing emissions, chemical production processes were optimized to lower the amount of utility work and equipment investment expenses with increasing the system’s capacity for heat recovery. A sensitivity analysis and the energy efficiency analysis with pinch technique were performed on the distillation and purification of the 30 kt/a isopropyl acetate (IPAC) production process by using process simulation software of Aspen Plus. The IPAC refining tower optimization results show that the purity of the refined IPAC could be reached 99.9% at circumstances of 44 theoretical plates, 19 feed plates, and 0.755 reflux ratio. According to the optimized energy consumption data from Aspen Energy Analyzer (AEA), the cold and heat logistics matching was performed. It can be seen that the heat exchange network was tuned to maximize energy recovery by reducing the amount of utility work. The optimized cold and heat utility usage were 734.69 and 727.81 kW, which meaning that compared with original process, the cold and heat utility usage energy can be save with 10.0%, respectively. The optimized results provide a certain theoretical basis and solution for improving energy saving and reducing investment costs. [ABSTRACT FROM AUTHOR]

Mammalian cells produce up to 80 % of the commercially available therapeutic proteins, with Chinese Hamster Ovary (CHO) cells being the primary production host. Manufacturing involves a train of reactors, the last of which is typically run in fed-batch mode, where cells grow and produce the required protein. The feeding strategy is decided a priori, from either past operations or the design of experiments and rarely considers the current state of the process. This work proposes a Model Predictive Control (MPC) formulation based on a hybrid kinetic-stoichiometric reactor model to provide optimal feeding policies in real-time, which is agnostic to the culture, hence transferable across CHO cell culture systems. The benefits of the proposed controller formulation are demonstrated through a comparison between an open-loop simulation and closed-loop optimization, using a digital twin as an emulator of the process.

In the priority model of sustainability, ecology is listed as the most important category of sustainability. A main goal is the decrease of carbon dioxide and other emissions to the environment. To tackle this challenge, the so called biologisation of manufacturing is identified as one lever for sustainability. In this paper, the example of a cooling lubricant system is considered and how this can be biologically transformed into a self-sufficient, fully-fledged process system that meets the requirements of a biologically transformed manufacturing system. To reach this objective, a present state of cooling lubricant systems, the weak points and optimization potential is presented. For the biologisation, various strategies are collected and categorized for implementation into lubricant systems. Further, the biologisation approaches are combined into a holistic concept by identifying synergies and interactions. The last step is a detailed elaboration and evaluation of transformation strategies. [ABSTRACT FROM AUTHOR]

Reliability-based and risk-based methods for directing maintenance activities play a critical role in ensuring system safety and reducing unnecessary downtime. Those methods focus on preventive maintenance to avoid component failures and are applicable before unexpected disruptions occur. However, when disruptions are unavoidable, more attention should be paid to systems' recovery from unwanted changes. As a remedy of preventive maintenance, improving system restoration capacity of resilience through optimizing the system's maintenance asset and operational cost is an efficient way to help system restore from disruption conditions within an optimal cost. In this paper, a resilience-based approach is proposed to optimize maintenance asset and operational cost. A novel resilience metric is developed and utilized to quantify system resilience under various restoration capacities. The minimal acceptable resilience level (MARL) and maximal acceptable restoration time (MART) are proposed to determine the optimal maintenance cost. The proposed approach is applied to the Chevron Richmond refinery crude unit and its upstream process. The results show that it can help practitioners identify the optimal cost to ensure a system is resilient to respond to uncertain disruptions and provide a dynamic resilience profile to support decision-making. [ABSTRACT FROM AUTHOR]

Due to the continuity and complexity of chemical systems, piping and operating conditions will have a significant effect on the on-line continuous weighing of materials inside process equipment. In this paper, a mathematical model of the weighing system considering piping and operating conditions was established based on the gas–liquid continuous heat transfer weighing process. A theoretical criterion which can be extended to any continuous weighing system of the materials inside equipment with connected piping is obtained through the mechanical derivation between the material mass, the cantilever beam deflection, the strain gage deformation, and the bridge output voltage. This criterion can effectively predict the influence of piping on weighing results with specific accuracy, and provide a basis for engineering optimization design. On this basis, a set of gas–liquid continuous contact weighing devices was built. The static/dynamic experimental results showed that the accuracy of the system meets the set requirements. [ABSTRACT FROM AUTHOR]

Background and aims: Process systems due to processed under severe operational conditions and deal with large amounts of flammable and explosive materials have always led to many catastrophic accidents. Risk assessment is a useful tool for designing effective strategies for preventing and controlling these accidents. Conventional risk assessment methods have major deficiencies, including uncertainty in the obtained results and the completely static nature, therefore, the present study is aimed at applying a dynamic and quantitative approach to assess the safety risks of city natural gas pressure regulating stations. Methods: First, according to the standard of the Total Company (GS EP SAF 253), the reference (credible) event scenario was determined, then a qualitative, quantitative and dynamic modeling of the cause – consequence accident scenario model using Bayesian Network (BN) is provided and next, deductive and abductive probabilistic reasoning are conducted by means of constructed BN model. PHAST 7.11 program is employed to modeled and evaluated of different consequences of the scenario. Finally, the risk of accident scenario consequences was calculated, evaluated and updated. Results: 43 root events in occurrence of the credible event scenario of the gas stations were identified. Among the identified causes, the human failures (85%), process failures (10%) and mechanical failures (5%) had the highest contributing to occurrence in the accident scenario, respectively. Occurrence probability of the scenario is determined 7.11 ×10-2. Safety barriers, especially emergency shutdown valves (ESD), had a significant role in reducing the consequences severity. The risk of all three of consequences including jet fire, flash fire and vapor cloud explosion is located in unacceptable area. Conclusion: The use of BNs provides a comprehensive qualitative, quantitative and dynamic graphical modeling of the accident scenario. The abductive reasoning ability of these networks is capable to reducing the uncertainty and updating the probability of occurrence of root events and final the consequences. Using BNs along with consequences modeling, leads to a slightly more dynamic, precise, and practical risk assessment in process plants.

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Due to the continuity and complexity of chemical systems, piping and operating conditions will have a significant effect on the on-line continuous weighing of materials inside process equipment. In this paper, a mathematical model of the weighing system considering piping and operating conditions was established based on the gas–liquid continuous heat transfer weighing process. A theoretical criterion which can be extended to any continuous weighing system of the materials inside equipment with connected piping is obtained through the mechanical derivation between the material mass, the cantilever beam deflection, the strain gage deformation, and the bridge output voltage. This criterion can effectively predict the influence of piping on weighing results with specific accuracy, and provide a basis for engineering optimization design. On this basis, a set of gas–liquid continuous contact weighing devices was built. The static/dynamic experimental results showed that the accuracy of the system meets the set requirements.

Highlights • A control-oriented modeling approach is proposed for interconnected CRNs (Chemical Reaction Networks), relying both on compartmental systems, and CRN theory. • A distributed setpoint control algorithm is introduced which assures that the concentration levels of the chemicals in each CRN in the interconnected system reach prescribed fixed points. • The proposed control algorithm is independent of the CRNs kinetics and the delay in the convection network. • An extended version of the control algorithm is also introduced which is able to attenuate the effect of unknown input disturbances on the control performance. Abstract This paper introduces a control approach for a class of Chemical Reaction Networks (CRNs) that are interconnected through a delayed convection network. First, a control-oriented model is proposed for interconnected CRNs. Second, based on this model, a distributed control method is introduced which assures that each CRN can be driven into a desired fixed point (setpoint) independently of the delay in the convection network. The proposed algorithm is also augmented with a disturbance attenuation term to compensate the effect of unknown input disturbances on setpoint tracking performance. The control design applies the theory of passive systems and methods developed for multi-agent systems. Simulation results are provided to show the applicability of the proposed control method. [ABSTRACT FROM AUTHOR]

Life cycle assessment (LCA) is a tool that aids in sustainable decision-making among product and process alternatives. When implementing LCA, the efficient and accurate modeling of chemical processes for life cycle inventory (LCI) generation is still challenging. Challenges include a lack of systematic design and simulation tools and approaches to develop chemical process models for obtaining and analyzing more realistic LCI results. In this contribution, a novel process systems framework is proposed for estimating LCI results when implementing pollution control technologies. This framework involves the development and incorporation of pollution control unit (PCU) modules into process simulation and generation of LCI data associated with the PCUs for use in a sustainability evaluation. Different pollution control modules are designed for rapid LCI estimation and applied to obtain emissions, utility consumption, material, and land footprint results related to waste streams of a process simulation. Then, the LCI results are analyzed with the objectives of minimizing the environmental impact and utility consumption. The proposed framework is illustrated via a biomass/coal gasification process for syngas production with the end goal of acetic acid manufacturing. Results associated with this case study show that the developed framework can provide guidelines for sustainable decision-making based on generated LCI results. [ABSTRACT FROM AUTHOR]

Recently, we have proposed a new formulation approach for control degree of freedom (CDOF) analysis of process systems. This formula interrelates the CDOF and elements of a process flow diagram (PFD). The formulation is accurate, easily applicable by process engineers, and needs little prior knowledge of the process under study. This paper describes further about this new formulation and its advantages by applying it to batch and cyclic processes. The results demonstrate the correctness of the method in determining CDOF for all case studies at both steady state and dynamic conditions. The CDOF for "reheat regenerative Rankine" and "vapor absorption refrigeration" cycles have been determined to be 9 and 8, respectively. The steady state CDOF values for the Rankine and refrigeration cycles have been calculated as 8 and 4, respectivley. Also the CDOF for the process of "heat pump integrated with batch distillation column" is 4 that verifies the suggested formula of CDOF. The method also gives beneficial insights about the manipulated variables (MVs) in a control system. [ABSTRACT FROM AUTHOR]

Abstract: For distillation columns, a temperature is controlled to hold product quality while the column is operated at constant pressure. In practice, absolute stability of temperature and pressure is unachievable, whereas the product quality, namely, product composition, fluctuates with the variation of them. By analyzing phase equilibrium, the composition fluctuation, caused by slight temperature and pressure variation, cannot be ignored for precise distillation column. Thus, a novel approach is proposed to improve the performance of product quality control. For preliminary, the freestanding pressure dynamic model is established via calculating vapor molar holdup. The modeling method is applied to a commercial ethylene column, and the veracity and reliability of the dynamic model is improved. For principal part, the complementary control strategy is proposed on the basis of the improved dynamic behaviors. Besides qualitative analysis, 2 scenes that contain tight control for external disturbance and transition control for internal regulating are discussed to verify the feasibility and progressiveness of product quality control with complementary structure. [ABSTRACT FROM AUTHOR]

This paper provides a perspective on the major challenges and directions in academic process control research over the next 5–10 years, and its industrial implementation. Large-scale systems control and identification, nonlinear model-based and model-free control, and controller performance monitoring and diagnosis are discussed as major directions for future research, along with control technology and industry workforce challenges and opportunities. • Major challenges and directions in process control research and development over the next 5–10 years are discussed. • Large-scale control and identification of nonlinear systems are considered as fundamental directions in model-based control. • Model-free methods are emphasized as promising approaches to integrates machine learning and process control. • Controller performance monitoring and diagnosis are of practical value for improving state-of-the-art control systems. • Control technology and industry workforce challenges and opportunities are also highlighted. [ABSTRACT FROM AUTHOR]

Reducing energy consumption and increasing ethylene recovery rate can effectively optimize the operation of ethylene columns. This paper discusses the trade-off between energy consumption and recovery rate in different scenes. For normal condition, the influences of energy consumption and recovery rate on economic benefits are represented as energy consumption of separation and energy consumption of circulation, while the unseparated ethylene is recycled by the bottom circulatory system. The operation optimization, whose objective is economic benefits, is achieved at the lowest total energy consumption, the sum of energy consumption of separation and circulation. For abnormal condition caused by furnace decoking, the objectives of operation optimization are economic benefits and operability. Operability, affected with remarkable production fluctuation, is quantified by variation of gas and liquid loads. Moreover, the Pareto curve of the multiobjective optimization problem was obtained with total energy consumption and variation of gas/liquid loads as objectives, and the inflection point achieves the equilibrium of economic benefits and operability. Therefore, the balance of recovery rate and energy consumption is crucial to improve economic benefits. Furthermore, moderately operating recovery rate and energy consumption is suitable to maximize economic benefits and operability simultaneously for production fluctuation. © 2017 Curtin University of Technology and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The energy for the solvent regeneration of post-combustion carbon capture (PCC) process is typically provided by steam bleeding from the power plant (PP) steam cycle. The energy penalty for steam bleeding results in serious reduction in the PP capacity estimated to be in the range of 10–40%. Power plant repowering or hybridization using solar-assisted PCC (SPCC) is a promising approach to satisfy carbon capture targets as well as PP load, concurrently. The drawback of this methodology is that notable amounts of solar energy are wasted during heat transfer from solar radiation to rich solvent. This paper presents a novel approach by eliminating the costly desorber system and using solar collector pipe (i.e. parabolic trough pipe) to directly heat the rich solvent and disassociate the bonds of CO 2 -solvent. This novel technology lowers the process capital expenditure, and also reduces the solvent regeneration energy bringing it close to its theoretical values. The elimination of the complex desorber column also increases the flexibility of the PP operation in response for market dynamics. A case-study for Sydney-Australia shows that in comparison with SPCC methodology, this state-of-the-art approach could notably improve the economics of the process and reduce the size of solar collector field (SCF). [ABSTRACT FROM AUTHOR]

In process systems, analysis of control degrees of freedom (CDOF) is a primary step for control structure design. CDOF is equal to the maximum number of variables that can be independently manipulated by the control system. This study develops a novel approach for systematic computation of CDOF through the identification of topological characteristics of process flow diagrams (PFDs). The analysis has a top-down approach in which the main set of potential manipulated variables (MVs) is decomposed into internal and boundary subsets, then CDOFs for the internal and boundary MVs are determine separately. In order to develop the method, three assisting terms have been introduced: (1) circuit, (2) bypass, and (3) route. The suggested formula computes both dynamic and steady state CDOF using basic information obtained from the plant’s PFD. Applicability and reliability of the proposed method have been successfully checked for various case studies ranging from simple units to complex process plants. [ABSTRACT FROM AUTHOR]

• Organization effect on safety and risk management. • Safety and Risk management in process industries and healthcare. • Human decision-making in safety critical operations. • Design of decision-support tools for effective human decision-making. • Process systems engineering applications in healthcare. Cyber-physical-human systems are ubiquitous entities with varied scales of function. Apart from the systems-level complexity, it also poses a variety of decision-making problems to designers and practitioners. This decision-making is done by humans-in-the-loop, at every level of cyber-physical-human systems, with the help of decision-support tools. For any safety and risk critical cyber-physical-human systems organization, certain key features are useful to assess and compare the organizational functioning. Two such examples are process and healthcare industries, with safety and risk as major objectives. The paper uses the lens of humans-in-the-loop decision-making and decision-support tools to survey how safety and risk are handled in process and healthcare industries. Further, a discussion of current practices in the two domains and how future applications will involve more interdisciplinary aspects is provided. The paper concludes by discussing the ethical and data related concerns of a humans-in-the-loop and algorithm based shared decision-making future. [ABSTRACT FROM AUTHOR]