1 - 20 |
A multi-objective mathematical model to redesign of global sustainable bioenergy supply network Razm S, Nickel S, Sahebi H |
21 - 34 |
Multi-objective optimization of sulfur recovery units using a detailed reaction mechanism to reduce energy consumption and destruct feed contaminants Rahman RK, Ibrahim S, Raj A |
35 - 52 |
Multi-objective biopharma capacity planning under uncertainty using a flexible genetic algorithm approach Jankauskas K, Farid SS |
53 - 68 |
A Gibbs energy-driving force method for the optimal design of non-reactive and reactive distillation columns Lopez-Arenas T, Mansouri SS, Sales-Cruz M, Gani R, Perez-Cisneros ES |
69 - 76 |
A scalable stochastic programming approach for the design of flexible systems Pulsipher JL, Zavala VM |
77 - 87 |
Integrated optimal design and scheduling for a bitumen upgrader facility Purkayastha SN, Gates ID, Trifkovic M |
88 - 105 |
A system dynamics model for optimal allocation of natural gas to various demand sectors Daneshzand F, Amin-Naseri MR, Asali M, Elkamel A, Fowler M |
106 - 116 |
The value-added of dual-stage entrained flow gasification and CO2 cycling in biomass-to-gasoline/diesel: Design and techno-economic analysis Yang SY, Yang YC, Liu YJ |
117 - 127 |
Campaign-based modeling for degradation evolution in batch processes using a multiway partial least squares approach Wu O, Bouaswaig A, Imsland L, Schneider SM, Roth M, Leira FM |
128 - 140 |
Model predictive control with active learning for stochastic systems with structural model uncertainty: Online model discrimination Heirung TAN, Santos TLM, Mesbah A |
141 - 163 |
On dealing with measured disturbances in the modifier adaptation method for real-time optimization Navia D, Puen A, Quintanilla P, Briceno L, Bergh L |
164 - 173 |
Chromatographic studies of n-Propyl Propionate, Part II: Synthesis in a fixed bed adsorptive reactor, modelling and uncertainties determination Nogueira IBR, Faria RPV, Rodrigues AE, Loureiro JM, Ribeiro AM |
174 - 187 |
Learning and predicting operation strategies by sequence mining and deep learning Dorgo G, Abonyi J |
188 - 200 |
Forecasting of process disturbances using k-nearest neighbours, with an application in process control Borghesan F, Chioua M, Thornhill NF |
201 - 215 |
Incorporating automation logic in online chemical production scheduling Rawlings BC, Avadiappan V, Lafortune S, Maravelias CT, Wassick JM |
216 - 227 |
A multi-objective optimization model for tactical planning of upstream oil & gas supply chains Attia AM, Ghaithan AM, Duffuaa SO |
228 - 245 |
A bilevel decomposition method for the simultaneous heat integration and synthesis of steam/organic Rankine cycles Elsido C, Martelli E, Grossmann IE |
246 - 260 |
A simultaneous optimization approach for efficiency measures regarding design and operation of industrial energy systems Hofmann R, Panuschka S, Beck A |
261 - 284 |
Efficient modeling of distributions of polymer properties using probability generating functions and parallel computing Asteasuain M |
285 - 300 |
Predictive models and detection methods applicable in water detection framework for industrial electric arc furnaces Alshawarghi H, Elkamel A, Moshiri B, Hourfar F |
301 - 311 |
Dynamic prediction of interface level using spatial temporal markov random field Liu ZY, Kodamana H, Afacan A, Huang B |
312 - 321 |
Towards energy-efficient LNG terminals: Modeling and simulation of reciprocating compressors Reddy HV, Bisen VS, Rao HN, Dutta A, Garud SS, Karimi IA, Farooq S |
322 - 328 |
Coordinated dual-hormone artificial pancreas with parallel control structure Moscardo V, Herrero P, Diez JL, Gimenez M, Rossetti P, Georgiou P, Bondia J |
329 - 339 |
Design for dynamic operation - A review and new perspectives for an increasingly dynamic plant operating environment Swartz CLE, Kawajiri Y |
340 - 351 |
Optimization of cellulose hydrolysis in a non-ideally mixed reactors Fenila F, Shastri Y |
352 - 363 |
Coordinated management of organic waste and derived products Sampat AM, Hu YC, Sharara M, Aguirre-Villegas H, Ruiz-Mercado G, Larson RA, Zavala VM |
364 - 383 |
Recent developments towards enhancing process safety: Inherent safety and cognitive engineering Srinivasan R, Srinivasan B, Igbala MU, Nemet A, Kravanja Z |
384 - 391 |
Control of cryogenic extractive distillation process for separating CO2-C2H6 azeotrope Wang HQ, Fan ML, Zhang ZB, Hao JY, Wang C |
392 - 404 |
Formation lithology classification using scalable gradient boosted decision trees Dev VA, Eden MR |
405 - 416 |
Multi-rate data-driven models for lactic acid fermentation - Parameter identification and prediction Gan JW, Parulekar SJ |
417 - 420 |
Optimization-based global structural identifiability Joy P, Djelassi H, Mhamdi A, Mitsos A |
421 - 436 |
Challenges and future directions for process and product synthesis and design Martin M, Adams TA |
437 - 449 |
SS-DAC: A systematic framework for selecting the best modeling approach and pre-processing for spectroscopic data Rato TJ, Reis MS |
450 - 467 |
Profitability, risk, and investment in conceptual plant design: Optimizing key financial parameters rigorously using NPV% Mellichamp DA |
468 - 476 |
Controllability of low-consistency chemical pulp refining process Olejnik K, Fabijanska A, Pelczynski P, Stanislawska A |
477 - 487 |
Modelling emerging pollutants in wastewater treatment: A Case study using the pharmaceutical 17 alpha-ethinylestradiol Acheampong E, Dryden IL, Wattis JAD, Twycross J, Scrimshaw MD, Gomes RL |
488 - 495 |
Dynamic real-time optimization of batch processes using Pontryagin's minimum principle and set-membership adaptation Paulen R, Fikar M |
496 - 511 |
Optimal design of integrated batch production and utility systems Leenders L, Bahl B, Lampe M, Hennen M, Bardow A |
512 - 523 |
Modeling and control of cell wall thickness in batch delignification Choi HK, Kwon JSI |
524 - 537 |
A steady-state precipitation model for flowsheet simulation and its application Rehage H, Scherer S, Kind M |
538 - 556 |
Process control practice and education: Past, present and future Bequette BW |
557 - 573 |
Combining the advantages of discrete- and continuous-time scheduling models: Part 2. systematic methods for determining model parameters Lee H, Maravelias CT |