Computer-aided process engineering (CAPE) plays a key design and operations role in the process industries, from the molecular scale through managing complex manufacturing sites. The research interests cover a wide range of interdisciplinary problems related to the current needs of society and industry. ESCAPE 23 brings together researchers and practitioners of computer-aided process engineering interested in modeling, simulation and optimization, synthesis and design, automation and control, and education. The proceedings present and evaluate emerging as well as established research methods and concepts, as well as industrial case studies. - Contributions from the international community using computer-based methods in process engineering - Reviews the latest developments in process systems engineering - Emphasis on industrial and societal challenges
In this work a multi-objective hybrid optimization strategy was developed considering genetic algorithms (GA) in series with sequential quadratic programming (SQP). This methodology is used to minimize carbon monoxide emissions of regenerator dense phase at the same time that maximize process conversion in Fluid Catalytic Cracking (FCC). The process is characterized for being a highly nonlinear with strong interactions between process variables. The combination of those optimization algorithms was developed considering final values of GA optimization as initial estimative of SQP algorithm. The reason for that is because initial estimative determined by a stochastic technique is not subject to local minimums and additionally, deterministic technique speed up the calculations and reach the final solution in shorter times in order to obtain optimization objectives with low computational burden and time.
This work presents a multi-period waste management multi-objective optimization, considering economic and environmental issues. The specific behavior of the considered waste management treatments is included in the optimization problem as black-box models based on practical industrial practice computing utility requirements and emissions. To achieve more realistic solutions, the estimation of waste treatment costs and environmental impacts has been explicitly added to the assessment scheme, as well as the constraints of the operating conditions in the treatment units and the fulfillment of environmental regulations for water and air emissions. This framework is applied to an industrial based case study and used to analyze the waste mixing potential. Two main strategies are proposed to tackle the problem using a rigorous mathematical problem formulation. The performance of both strategies is compared in terms of solution quality and computational complexity with the final aim of finding an efficient methodology for posing the heat and waste management integration problem in a future work.
CO2 capture and sequestration (CCS) is a major means to reduce greenhouse gas emissions, especially from centralized energy conversion sites. Many studies have been conducted either on the CO2 capture side or on the sequestration side, but research on CO2 transport, which connects the capture side and the sequestration side, is rather limited. CO2 capture sites and sequestration sites are usually not geographically located in the same region, thus planning and design of CO2 transport infrastructure must be considered before capture and sequestration activities are conducted. Previously, some studies have been conducted to address the optimal planning problem of CO2 pipelines network via a source-sink match approach, which is applicable to early stages of CCS development when the total amount of CO needed to be transported is rather small. However, in scenarios with large amount of CO to be transported, the source-sink match approach appears to be less efficient. In this manuscript, we propose a superstructure based modelling and optimization framework for optimal planning of CO2 transport pipelines network, where all CO2 emissions sources and sequestrations sites can be connected by pipelines, and pump stations can also be built along pipelines to maintain the pressure of CO transported. We also provide a case study of a region in North China, with 45 emissions sources and four sequestration sites. Different configurations of pipelines network are illustrated in scenarios with low, medium, and high CO2 capture rates.
A bioreactor, integrated with an anoxic reactor and a settler for waste water treatment from a paper production plant is under investigation to implement a control system for enhancing effluent quality. In order to reveal the operation of the integrated process to achieve a specific goal, a methodology for control system development is proposed. In this paper, preliminary results of some steps of the methodology are presented, in order to address the oxygen uptake rate control. A dynamic model is developed for future analysis for the conceptual design of different generated control configurations.
A major bottleneck in state-of-the-art algorithms for global dynamic optimization using complete methods is computing enclosures for the solutions of nonlinear parametric differential equations. This paper presents a new algorithm for computing such enclosures, which features a combination of Taylor model propagation and ellipsoidal calculus. The former enables high-order convergence to the exact reachable set as the parameter set shrinks, while the latter mitigates bound explosion compared to interval analysis. The convergence properties of the proposed bounding technique are analyzed and conditions under which higher order convergence can be achieved are given. Implementation details are also discussed and the approach is demonstrated on a numerical case study.
The environment safety and climate changes reduction are two important aspects which partially can be achieved by lowering gaseous emissions emerged from chemical industry. These emissions are characterized by low pollutant concentration, high volumes of gases and low temperatures. Their treatment could emerge in economical sustainable technologies only if the reactors used are capable of high yields, high selectivity and autothermal behavior. The forced unsteady-state reactors are an excellent choice for treating this type of emissions because allow trapping of the moving heat wave inside the catalytic bed when exothermic reactions take place; the catalytic bed acts as a regenerative heat exchanger creating the premises of a sustain autothermal behavior even if the temperature of the feed is low. Nevertheless, the forced unsteady state reactors are characterized by a remarkable complex dynamic behavior, as a consequence of the complex interaction between heat and mass transport phenomena inside the reactor. This dynamic behavior gives rise to a set of spatial-temporal patterns, chaotic changes in concentration and traveling waves of heat and chemical reactivity, difficult to deal with. Therefore, an advance control strategy is needed to be applied in order to tackle with all issues that characterize reactors with improved contact between reactants, in which the thermal wave is stored and which allow improved kinetic activity of the catalyst used. The catalytic activity is the main operation concern for processes with low feed temperatures and constraints related to low emissions of unconverted reactants. Therefore, predictions of reactor pseudo or steady-state performances regarding conversion, selectivity and thermal behavior and the dynamic reactor response during exploitation are important aspects in finding the optimal control strategy. Moreover, the switching time and the reagents concentrations in the feeding zone are the main operating parameters that may be adapted or changed to fulfill the condition of zero emissions when using forced unsteady-state reactors. Nevertheless, due to unsteady state reactors characteristics, the adaptation of these parameters to process requirements reveals important control design problems. Therefore, in this paper the possibility of using an advanced control strategy, combining model predictive control (MPC) with an on-off control was studied in order to handle with problems that arise when using a forced unsteady-state catalytic reactor network (RN) in case of selective catalytic reduction of NOx with ammonia. The RN system consists of a network of three plug-flow reactors in series, operated in a closed loop configuration. The RN dynamic model was developed using MatLab/Simulink software. The results revealed that the proposed control strategy (i.e. the on-off control and MPC) can successfully handle the NOx emissions of this kind of reactors.
Wastewater treatment is an important process of considerable significance for environmental, economic and social aspects of sustainability. Although wastewater treatment helps reduce environmental pollution, thus limiting negative societal effects, it may have negative impacts on sustainability; for instance, waste discharged during the treatment. Determination of the impact of a particular process generally involves application of a set of sustainability indicators that evaluate environmental, economic and societal issues. However, indicators are usually presented without a description that is sufficiently detailed to enable repetition of the calculations. For example, sustainability indicators for evaluation of wastewater treatment processes are commonly presented in tabular form. The objective of this work is to assess a set of sustainability indicators for evaluation of environmental, economic and societal aspects of wastewater treatment processes. Some of the presented indicators have not previously been used in the context of wastewater treatment. Usually environmental sustainability is assessed through calculation of greenhouse gas release, waste discharge and energy use. Economic sustainability is determined by estimating capital and wastewater operating costs. Social sustainability is evaluated based on working conditions, e. g., worker stress, work satisfaction and attitudes to achieving sustainability. The applicability of the approach and indicators chosen is evaluated with case studies of wastewater treatment for which the presented sustainability indicators are calculated. The results show that environmental and economic indicators can easily be determined quantitatively, but social indicators require a qualitative approach. The usability of the indicators and their limitations are critically analyzed.
This study presents a business model of continuous improvement in pharmaceutical production processes. The model covers the lifecycle of an improvement, i.e. opportunity identification, project execution and change implementation, with considering various pharma-specific constraints such as GMP (Good Manufacturing Practice). The model is described as so-called an activity model using IDEF0 (Integrated DEFinition type 0) in order to define activities and sub-activities in a clear and consistent manner. A roll-defining model termed RACI (responsible, accountable, consulted and informed) is uniquely combined with the IDEF0 model, for characterizing roles of various stakeholders in each improvement step. The model was developed and applied in a new sterile drug product manufacturing facility of Roche in Kaiseraugst, Switzerland. In this start-up facility, a case study was performed for reducing losses of valuable biologics drug products with a continuous and organization-wide effort.
We demonstrate an approach to construct an adaptive design space in the face of process variability, for an industrial hydrophobic interaction chromatography (HIC) with resin lot-to-lot variability. The step has a complex mixture of impurities in the feed stream and a multi component product. The step must deliver a specific distribution of product forms in the elution peak whilst maintaining product recovery and impurity removal. In our approach, a mechanistic model is used which gives a good representation of the system, and has been validated experimentally. The model is used to quickly and efficiently explore the impact of process parameters on process performance, utilizing stochastic simulation to generate probabilistic design spaces for different resin lots. The results indicate that significant increases in process robustness can be made by adapting the design space based on the resin lot in use, rather than fixing the design space for all resin lots. An adaptive design space enables operation further away from high risk regions, increases the size of operating regions and improves flexibility to variations in process inputs.
