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

Hydrogen is considered to be one clean energy carrier for the future. The main advantage of using hydrogen as energy carrier represents the negligible greenhouse gas emissions. One of the most encouraging hydrogen production methods is based on bioethanol. This paper is devoted to the conceptual design of hydrogen production process using bioethanol reforming at an industrial scale (100000 Nm3/h hydrogen equivalent to 300 MW thermal). The syngas will be then converted into hydrogen by water gas shift reaction. Two distinct designs were investigated, one without carbon capture and one with carbon capture. In the design with carbon capture and storage (CCS), the shifted syngas is treated for removing CO2 by gas-liquid absorption process. Considering that bioethanol has a low or negligible fossil carbon footprint, the case with CCS has a negative CO2 emissions contributing to decreasing atmospheric CO2. The conceptual designs of bioethanol reforming were modeled and simulated to produce mass and energy balances for quantification of key plant performance indicators (e.g. bioethanol consumption, plant energy efficiency, ancillary energy consumption, specific CO2 emissions etc.). A particular accent is put on assessment of reforming unit operation conditions, process integration issues of reforming unit and syngas conditioning line with carbon capture unit, modeling and simulation of whole plant, thermal and power integration of various plant sub-systems by pinch analysis. Benchmark cases based on fossil fuels partial oxidation were also presented.

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.

Real-time optimization (RTO) has become a standard practice to improve production benefits during the past years. The efficiency of solving optimization problems is critical because a large computational delay leads to the possible loss of validity and availability of RTO. In this study, the Mnemonic Enhancement Optimization (MEO) strategy of initialization for RTO has been extended by taking advantage of optimal sensitivity. The approximation precision and the solution information database accumulation efficiency of the proposed sensitivity-based MEO are briefly analyzed. The numerical results tested with a high-pressure column of a cryogenic air separation unit were in agreement with the theoretical analysis.

Environmental process design methods often incorporate life-cycle assessment (LCA) as a deterministic black-box methodology. However, LCA suffers from several sources of ambiguity and uncertainty. These limitations are particularly severe for the multi-functional processes typical for the chemical industry. This work introduces a rigorous uncertainty assessment framework. The framework quantifies data uncertainties and also uncertainties that result from fixing multi-functionality in LCA by allocation. The method is derived from a matrix-framework for LCA and applied to a comparison of alternative chlorine electrolysis technologies.

In the context of greenhouse gas emissions mitigation, CO2 capture and storage is regarded as a promising alternative for fossil fuel fed power plants. In order to design and evaluate the competitiveness of such complex integrated energy conversion systems, a systematic comparison including thermodynamic, economic and environmental considerations is necessary. From a process engineering perspective, it is important to evaluate the impact of CO2 capture not only on the efficiency but also on the costs and on the environmental impacts, and to assess the trade-offs. This paper presents the development of a systematic thermo-environomic optimisation strategy for the consistent modelling, comparison and optimisation of fuel decarbonisation options. In particular, it is highlighted how the economic scenario influences the competitiveness and hence the optimal process design.

Reliable models for rate-based phenomena are the backbone of model-based process design. These models are often unknown in the early design phase and need to be determined from laboratory experiments. Although model-based experimental analysis and process design are often executed sequentially, the kinetic models might not be suitable to reliably design a process. In this paper, we address this problem and present a first step on the integration of model identification and process optimization. Rather than decoupling model identification and process optimization, we use information from process optimization to design optimal experiments for improving the quality of the kinetic model given the intended use of the model. Sensitivities, which describe the influence of parametric uncertainties on the economic objective used in process optimization, are used as weights for optimal experimental design. This way, the confidence in the parameter values is maximized to reduce their influence on the process optimization objective. This first step on the integration of model identification and process optimization improves the predictive quality of a reaction kinetic model for process design without any further experimental effort.

The plant input variables are transformed into the derived variables, which are physically and statistically meaningful for modeling. Then, this study proposed an adaptive modeling approach by employing nonnegative garrote variable selection and auto-regression integrated moving average correction. The high accuracy of the proposed modeling approaches makes the implementation of the model-based control of ratio of air to fuel for improving boiler's efficiency readily practicable.

This work focuses on the development of a computer-aided modeling framework. The framework is a knowledge-based system that is built on a generic modeling language and structured on workflows for different modeling tasks. The overall objective of this work is to support model developers and users to generate and test models systematically, efficiently and reliably. In this way, development of products and processes can be made faster, cheaper and more efficient. In this contribution, as part of the framework a generic modeling template for the systematic derivation of problem specific catalytic membrane fixed bed reactor models is presented. The application of the modeling template is highlighted with a case study related to the modeling of a catalytic membrane reactor coupling dehydrogenation of ethylbenzene with hydrogenation of nitrobenzene.

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.