This work focuses on designing the optimum raceway pond by considering the effects of sunlight availability, temperature fluctuations, and harvest time on algae growth, and introduces a dynamic programing model to do so. Culture properties such as biomass productivity, growth rate, and concentration, and physical properties, such as average velocity, pond temperature, and rate of evaporation, were estimated daily depending on the dynamic behavior of solar zenith angle, diurnal pattern of solar irradiance, and temperature fluctuations at the location. Case studies consider two algae species (Phaeodactylum. tricornutum and Isochrysis. galbana) and four locations (Tulsa, USA; Hyderabad, India; Cape Town, South Africa; and Rio de Janeiro, Brazil). They investigate the influences of the type of algae strain and geographical location on algae biomass production costs. From our case studies, the combination of I. galbana species grown in Hyderabad, India, with a raceway pond geometry of 30 cm... [more]

Polymer processes often contain state variables whose distributions are multimodal; in addition, the models for these processes are often complex and nonlinear with uncertain parameters. This presents a challenge for Kalman-based state estimators such as the ensemble Kalman filter. We develop an estimator based on a Gaussian mixture model (GMM) coupled with the ensemble Kalman filter (EnKF) specifically for estimation with multimodal state distributions. The expectation maximization algorithm is used for clustering in the Gaussian mixture model. The performance of the GMM-based EnKF is compared to that of the EnKF and the particle filter (PF) through simulations of a polymethyl methacrylate process, and it is seen that it clearly outperforms the other estimators both in state and parameter estimation. While the PF is also able to handle nonlinearity and multimodality, its lack of robustness to model-plant mismatch affects its performance significantly.

This work presents a methodology to represent logical decisions in differential algebraic equation simulation and constrained optimization problems using a set of continuous algebraic equations. The formulations may be used when state variables trigger a change in process dynamics, and introduces a pseudo-binary decision variable, which is continuous, but should only have valid solutions at values of either zero or one within a finite time horizon. This formulation enables dynamic optimization problems with logical disjunctions to be solved by simultaneous solution methods without using methods such as mixed integer programming. Several case studies are given to illustrate the value of this methodology including nonlinear model predictive control of a chemical reactor using a surge tank with overflow to buffer disturbances in feed flow rate. Although this work contains novel methodologies for solving dynamic algebraic equation (DAE) constrained problems where the system may experience... [more]

Chemical processes with complex reaction mechanisms generally lead to dynamic models which, while beneficial for predicting and capturing the detailed process behavior, are not readily amenable for direct use in online applications related to process operation, optimisation, control, and troubleshooting. Surrogate models can help overcome this problem. In this research article, the first part focuses on obtaining surrogate models for emulsion copolymerization of nitrile butadiene rubber (NBR), which is usually produced in a train of continuous stirred tank reactors. The predictions and/or profiles for several performance characteristics such as conversion, number of polymer particles, copolymer composition, and weight-average molecular weight, obtained using surrogate models are compared with those obtained using the detailed mechanistic model. In the second part of this article, optimal flow profiles based on dynamic optimisation using the surrogate models are obtained for the product... [more]

This paper discusses the initial steps towards the formulation and implementation of a generic and flexible model centric framework for integrated simulation, estimation, optimization and feedback control of polymerization processes. For the first time it combines the powerful capabilities of the automatic continuous on-line monitoring of polymerization system (ACOMP), with a modern simulation, estimation and optimization software environment towards an integrated scheme for the optimal operation of polymeric processes. An initial validation of the framework was performed for modelling and optimization using literature data, illustrating the flexibility of the method to apply under different systems and conditions. Subsequently, off-line capabilities of the system were fully tested experimentally for model validations, parameter estimation and process optimization using ACOMP data. Experimental results are provided for free radical solution polymerization of methyl methacrylate.

The editors of Processes would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2015. [...]

A nonlinear reduced-order state observer is applied to estimate the degree of polymerization in a series of polycondensation reactors. The finishing stage of polyethylene terephthalate synthesis is considered in this work. This process has a special structure of lower block triangular form, which is properly utilized to facilitate the calculation of the state-dependent gain in the observer design. There are two possible on-line measurements in each reactor. One is continuous, and the other is slow-sampled with dead time. For the slow-sampled titration measurement, inter-sample behavior is estimated from an inter-sample output predictor, which is essential in providing continuous corrections on the observer. Dead time compensation is carried out in the same spirit as the Smith predictor to reduce the effect of delay in the measurement outputs. By integrating the continuous-time reduced-order observer, the inter-sample predictor and the dead time compensator together, the degree of polym... [more]

This study aimed to develop optimal microwave assisted extraction conditions for recovery of phenolic compounds and antioxidant properties from the macadamia skin, an abundant waste source from the macadamia industry. Water, a safe, accessible, and inexpensive solvent, was used as the extraction solvent and Response Surface Methodology (RSM) was applied to design and analyse the conditions for microwave-assisted extraction (MAE). The results showed that RSM models were reliable for the prediction of extraction of phenolic compounds and antioxidant properties. Within the tested ranges, MAE radiation time and power, as well as the sample-to-solvent ratio, affected the extraction efficiency of phenolic compounds, flavonoids, proanthocyanidins, and antioxidant properties of the macadamia skin; however, the impact of these variables was varied. The optimal MAE conditions for maximum recovery of TPC, flavonoids, proanthocyanidins and antioxidant properties from the macadamia skin were MAE ti... [more]

Kinetic modeling of the bulk copolymerization of d-limonene (Lim) and n-butyl acrylate (BA) at 80 °C was performed using PREDICI®. Model predictions of conversion, copolymer composition and average molecular weights are compared to experimental data at five different feed compositions (BA mol fraction = 0.5 to 0.9). The model illustrates the significant effects of degradative chain transfer due to the allylic structure of Lim as well as the intramolecular chain transfer mechanism due to BA.

Integrated process and control design approach for cyclic distillation columns is proposed. The design methodology is based on application of simple graphical design approaches, known from simpler conventional distillation columns. Here, a driving force approach and McCabe-Thiele type analysis is combined. It is demonstrated, through closed-loop and open-loop analysis, that operating the column at the largest available driving force results in an optimal design in terms of controllability and operability. The performance of a cyclic distillation column designed to operate at the maximum driving force is compared to alternative sub-optimal designs. The results suggest that operation at the largest driving force is less sensitive to disturbances in the feed and inherently has the ability to efficiently reject disturbances.

Six Aspen Plus simulation files for the conversion of petroleum coke and/or natural gas to liquid fuels (synthetic gasoline and diesel) are presented. The base simulation files were designed with carbon capture and sequestration (CCS) technology with the corresponding plant without CCS.

The processes may include various technologies such as petcoke gasification, integrated gasification and autothermal natural gas reforming, gas cleaning, water gas shift reaction, MDEA based carbon capture, Claus process, FT synthesis, and other processing steps.

The six processes are: PSG_CCS (petcoke standalone gasification with CCS), PSG_No_CCS (petcoke standalone gasification without CCS), PG-INGR_CCS (petcoke gasification integrated natural gas reformer with CCS), PG-INGR_No_CCS (petcoke gasification integrated natural gas reformer without CCS), PG-ENGR_CCS (petcoke gasification external natural gas reformer with CCS), PG-ENGR_No_CCS (petcoke gasification external natural gas reformer with... [more]

This is the LAPSE stakeholder report for 2018, including news, new features, and the plan for the next year.

Dette er det LAPSE interessenter rapport for 2018, inkludert nyheter, nye funksjoner, og planen for neste år.

This paper describes the progress of our efforts to lead the CACHE (Computer Aids for Chemical Engineering Education) Task Force in transforming from chemical process design toward chemical product design. Through CACHE, we are coordinating the development of a library of product-design case studies. Beginning with preliminary product designs created previously over several semesters, we are arranging for faculty experts, knowledgeable in the underlying technology platforms, to work with student groups to enrich the product designs. Over a 3-year period, a collection of approximately 25 case studies is being prepared. This article describes the research envisioned as innovative product designs are created, both egarding applications of new technologies, and product design evolution/evaluation; and in advancing strategies for teaching product design. The anticipated use of these case studies in departments worldwide for design courses taught by similar technology experts, just a few in... [more]

Artificial neural networks (ANNs) are used in various applications for data-driven black-box modeling and subsequent optimization. Herein, we present an efficient method for deterministic global optimization of ANN embedded optimization problems. The proposed method is based on relaxations of algorithms using McCormick relaxations in a reduced-space [\textit{SIOPT}, 20 (2009), pp. 573-601] including the convex and concave envelopes of the nonlinear activation function of ANNs. The optimization problem is solved using our in-house global deterministic solver MAiNGO. The performance of the proposed method is shown in four optimization examples: an illustrative function, a fermentation process, a compressor plant and a chemical process optimization. The results show that computational solution time is favorable compared to the global general-purpose optimization solver BARON.