Multicriteria optimization of the composition, thermodynamic and strength properties of fly-ash as an additive in metakaolin-based geopolymer composites

This paper presents the construction of intelligent systems for selecting the optimum concentration of geopolymer matrix components based on ranking optimality criteria. A peculiarity of the methodology is replacing discrete time intervals with a sequence of states. Markov chains represent a synthetic property accumulating heterogeneous factors. The computational basis for the calculations was the digitization of experimental data on the strength properties of fly ashes collected from thermal power plants in the Czech Republic and used as additives in geopolymers. A database and a conceptual model of priority ranking have been developed, that are suitable for determining the structure of relations of the main factors. Computational results are presented by studying geopolymer matrix structure formation kinetics under changing component concentrations in real- time. Multicriteria optimization results for fly-ash as an additive on metakaolin-based geopolymer composites show that the optimal composition of the geopolymer matrix within the selected variation range includes 100 g metakaolin, 90 g potassium activator, 8 g silica fume, 2 g basalt fibers and 50 g fly ash by ratio weight. This ratio gives the best mechanical, thermal, and technological properties.

Multicriteria analysis of sewage sludge-based biodiesel production

There is increasing attention on developing efficient processes including circular economy principles, and obtaining fuels from wastewater treatment feedstocks is among the most promising. As a wastewater treatment byproduct, sewage sludge is a source of lipids that can be converted to biodiesel in a transesterification process. Economic and environmental analysis have been applied to a 60 m3/h sewage sludge plant, exploring 32 process alternatives. Using solvent extraction from wet sewage sludge, the high cost associated with the drying step is skipped. The wet alternatives with low amounts of solvent and acid usage depicted higher performance compared to the dry ones. Incorporating additional extraction stages increases both the financial gains and environmental impacts. As a result, a multicriteria analysis is implemented to ascertain the optimum process based on different priorities. The case with 0.5:1 (v/v) of hexane to biomass ratio, 3-stage extractor, 60 min residence time and pH 4 was the optimum alternative in most criteria.

Evaluating Pareto optimal tradeoffs for hippocampal avoidance whole brain radiotherapy with knowledge-based multicriteria optimization

The goal of this study is to investigate the Pareto optimal tradeoffs between target coverage and hippocampal sparing using knowledge-based multicriteria optimization (MCO). Ten prior clinical cases were selected that were treated with hippocampal avoidance whole brain radiotherapy (HA-WBRT) using VMAT. A new, balanced plan was generated for each case using an in-house RapidPlan model in the Eclipse V16.1 treatment planning system. The MCO decision support tool was used to create 4 Pareto optimal plans. The Pareto optimal plans were created using PTV Dmin and hippocampus Dmax as tradeoff criteria. The tradeoff plans were generated for each patient by adjusting PTV Dmin from the value achieved by the corresponding balanced plan in fixed intervals as follows: -4 Gy, -2 Gy, +2 Gy, and +4 Gy. All plans were normalized so that 95% of the PTV was covered by the prescription dose. A 1-way ANOVA, with Geisser-Greenhouse correction, was used for statistical analysis. When evaluating the achieved PTV Dmin and D98%, the results showed the dose to the hippocampus decreased as coverage lowered and in comparison, D98% was higher when the PTV coverage was increased. When comparing multiple tradeoffs, the p-value for PTV D98% was 0.0026, and the p-values for PTV D2%, PTV Dmin, Hippocampus Dmax, Dmin, and Dmean were all less than 0.0001, indicating that the tradeoff plans achieved statistically significant differences. The results also showed that Pareto optimal plans failed to reduce hippocampal dose beyond a certain point, indicating more limited achievability of the MCO-navigated plans than the interface suggested. This study presents valuable data for planning results for HA-WBRT using MCO. MCO has shown to be mostly effective in adjusting the tradeoff between PTV coverage and hippocampal dose.

The design of an energy plant and artificial intelligence-based optimization for pasteurization with the least amount of carbon emissions based on animal waste

It has been known for a very long time that chemical energy may be converted into electrical energy by using biomass, considered a renewable energy source. In the study that is being presented here, an explanation and a presentation are offered on a one-of-a-kind hybrid system that generates dependable power and cooling by harnessing the chemical energy of biomass. An anaerobic digester takes in organic material and converts it into biomass by using the high-energy content of cow manure as fuel. The Rankin cycle is the primary engine that drives the system that produces energy, and its combustion-based byproducts are routed to an ammonia absorption refrigeration system in order to provide sufficient cooling for the process of pasteurizing and drying the milk. It is expected that solar panels might contribute to the production of sufficient amounts of power for necessary activities. The technical and financial facets of the system are both being investigated at the moment. In addition, the optimal working conditions are determined by employing a forward-thinking multi-objective optimization strategy. This method simultaneously raises the operational effectiveness to the greatest extent that is practically possible while simultaneously lowering both expenses and emissions. The findings indicate that under ideal conditions, the levelized cost of the product (LCOP), efficiency, and emission of the system are, respectively, 0.087 $/kWh, 38.2%, and 0.249 kg/kWh. The digester and the combustion chamber both have very high exergy destruction rates, with the digester having the highest rate and the combustion chamber having the second-highest rate among all of the system's components. This assertion is supported by every one of these components.

Optimized L-SNEDDS and spray-dried S-SNEDDS using a linked QbD-DM3 rational design for model compound ketoprofen

A QbD-DM³ strategy was used to design ketoprofen (KTF) optimized liquid (L-SNEDDS) and solid self-nanoemulsifying drug delivery systems (S-SNEDDS). Principal component analysis was used to identify the optimized L-SNEDDS containing Capmul® MCM NF, 10 % w/w; Kolliphor® ELP, 60 % w/w; and propylene glycol, 30 % w/w. The S-SNEDDS was manufactured by spray-drying a feed dispersion prepared by dissolving the optimized KTF-loaded L-SNEDDS in an ethanol-Aerosil® 200 dispersion. A Box Behnken design was employed to evaluate the effect of drug concentration (DC), Aerosil® 200 concentration (AC) and feed rate (FR) on maximizing percent yield (PY) and loading efficiency (LE). The optimal levels of DC, AC, and FR were 19.9 % w/w, 30.0 % w/w, and 15.0 %, respectively. The optimized S-SNEDDS was amorphous, and its dissolution showed a 2.37-fold increase in drug release compared to KTF in 0.1 HCl. An optimized independent spray-dried S-SNEDDS verification batch showed that the predicted and observed PY and LE were 70.49 % and 92.49 %, and 70.02 % and 91.27 %, respectively. The optimized L-SNEDDS and S-SNEDDS also met their quality target product profile criteria for globule size <100 nm, polydispersity index < 0.400, emulsification time < 30 s, and KTF L-SNEDDS solubility 100-fold greater than its water solubility.

Prediction of multi-criteria optimization (MCO) parameter efficiency in volumetric modulated arc therapy (VMAT) treatment planning using machine learning (ML)

PURPOSE

To predict the impact of optimization parameter changes on dosimetric plan quality criteria in multi-criteria optimized volumetric-modulated-arc therapy (VMAT) planning prior to optimization using machine learning (ML).

METHODS

A data base comprising a total of 21,266 VMAT treatment plans for 44 cranial and 18 spinal patient geometries was generated. The underlying optimization algorithm is governed by three highly composite parameters which model a combination of important aspects of the solution. Patient geometries were parametrized via volume- and shape properties of the voxel objects and overlap-volume histograms (OVH) of the planning-target-volume (PTV) and a relevant organ-at-risk (OAR). The impact of changes in one of the three optimization parameters on the maximally achievable value range of five dosimetric properties of the resulting dose distributions was studied. To predict the extent of this impact based on patient geometry, treatment site, and current parameter settings prior to optimization, three different ML-models were trained and tested. Precision-recall curves, as well as the area-under-curve (AUC) of the resulting receiver-operator-characteristic (ROC) curves were analyzed for model assessment.

RESULTS

Successful identification of parameter regions resulting in a high variability of dosimetric plan properties depended on the choice of geometry features, the treatment indication and the plan property under investigation. AUC values between 0.82 and 0.99 could be achieved. The best average-precision (AP) values obtained from the corresponding precision/recall curves ranged from 0.71 to 0.99.

CONCLUSIONS

Machine learning models trained on a database of pre-optimized treatment plans can help finding relevant optimization parameter ranges prior to optimization.

Evaluating the application of Pareto navigation guided automated radiotherapy treatment planning to prostate cancer

BACKGROUND AND PURPOSE

Current automated planning methods do not allow for the intuitive exploration of clinical trade-offs during calibration. Recently a novel automated planning solution, which is calibrated using Pareto navigation principles, has been developed to address this issue. The purpose of this work was to clinically validate the solution for prostate cancer patients with and without elective nodal irradiation.

MATERIALS AND METHODS

For 40 randomly selected patients (20 prostate and seminal vesicles (PSV) and 20 prostate and pelvic nodes (PPN)) automatically generated volumetric modulated arc therapy plans (VMAT_Auto) were compared against plans created by expert dosimetrists under clinical conditions (VMAT_Clinical) and no time pressures (VMAT_Ideal). Plans were compared through quantitative comparison of dosimetric parameters and blind review by an oncologist.

RESULTS

Upon blind review 39/40 and 33/40 VMAT_Auto plans were considered preferable or equal to VMAT_Clinical and VMAT_Ideal respectively, with all deemed clinically acceptable. Dosimetrically, VMAT_Auto, VMAT_Clinical and VMAT_Ideal were similar, with observed differences generally of low clinical significance. Compared to VMAT_Clinical, VMAT_Auto reduced hands-on planning time by 94% and 79% for PSV and PPN respectively. Total planning time was significantly reduced from 22.2 mins to 14.0 mins for PSV, with no significant reduction observed for PPN.

CONCLUSIONS

A novel automated planning solution has been evaluated, whose Pareto navigation based calibration enabled clinical decision-making on trade-off balancing to be intuitively incorporated into automated protocols. It was successfully applied to two sites of differing complexity and robustly generated high quality plans in an efficient manner.

Comparison of two beam angular optimization algorithms guided by automated multicriterial IMRT

PURPOSE

To compare two beam angle optimization (BAO) algorithms for coplanar and non-coplanar geometries in a multicriterial optimization framework.

METHODS

40 nasopharynx patients were selected for this retrospective planning study. IMRT optimized plans were produced by Erasmus-iCycle multicriterial optimization platform. Two different algorithms, based on a discrete and on a continuous exploration of the space search, algorithm i and B respectively, were used to address BAO. Plan quality evaluation and comparison were performed with SPIDERplan. Statistically significant differences between the plans were also assessed.

RESULTS

For plans using only coplanar incidences, the optimized beam distribution with algorithm i is more asymmetric than with algorithm B. For non-coplanar beam optimization, larger deviations from coplanarity were obtained with algorithm i than with algorithm B. Globally, both algorithms presented near equivalent plan quality scores, with algorithm B presenting a marginally better performance than algorithm i.

CONCLUSION

Almost all plans presented high quality, profiting from multicriterial and beam angular optimization. Although there were not significant differences when average results over the entire sample were considered, a case-by-case analysis revealed important differences for some patients.

Utilisation of Pareto navigation techniques to calibrate a fully automated radiotherapy treatment planning solution

Background and purpose

Current automated radiotherapy planning solutions do not allow for the intuitive exploration of different treatment options during protocol calibration. This work introduces an automated planning solution, which aims to address this problem through incorporating Pareto navigation techniques into the calibration process.

Materials and methods

For each tumour site a set of planning goals is defined. Utilising Pareto navigation techniques an operator calibrates the solution through intuitively exploring different treatment options: selecting the optimum balancing of competing planning goals for the given site. Once calibrated, fully automated plan generation is possible, with specific algorithms implemented to ensure trade-off balancing of new patients is consistent with that during calibration. Using the proposed methodology the system was calibrated for prostate and seminal vesicle treatments. The resultant solution was validated through quantitatively comparing the dose distribution of automatically generated plans (VMAT_Auto) against the previous clinical plan, for ten randomly selected patients.

Results

VMAT_Auto yielded statistically significant improvements in: PTV conformity indices, high dose bladder metrics, mean bowel dose, and the majority of rectum dose metrics. Of particular note was the reduction in mean rectum dose (median 25.1 Gy vs. 27.5 Gy), rectum V_24.3Gy (median 41.1% vs. 46.4%), and improvement in the conformity index for the primary PTV (median 0.86 vs. 0.79). Dosimetric improvements were not at the cost of other dose metrics.

Conclusions

An automated planning methodology with a Pareto navigation based calibration has been developed, which enables the complex balancing of competing trade-offs to be intuitively incorporated into automated protocols.

A new strategy for volumetric-modulated arc therapy planning using AutoPlanning based multicriteria optimization for nasopharyngeal carcinoma

BACKGROUND

A new strategy for making the appropriate choice of the representative optimization parameters in planning processes and accurate selection criteria during Pareto surface navigation for general multicriteria optimization (MCO) was recommended in the study. The purpose was to combine both benefits of AutoPlanning optimization and MCO (APMCO) for achieving an individual volumetric-modulated arc therapy (VMAT) plan according to the clinically achieved patient-specific tradeoff among conflicting priorities. The preclinical investigation of this optimization approach for nasopharyngeal carcinoma (NPC) radiotherapy was performed and compared to general MCO VMAT.

METHODS

A total of 60 NPC patients with various stages were enrolled in this study. General MCO and APMCO plans were generated for each patient on the treatment planning system. The differences between two planning schemes were evaluated and compared.

RESULTS

All plans were capable of achieving the prescription requirement. The planning target volume coverage and conformation number were remarkably similar between general MCO and APMCO plans. There were no significant differences in most of organs at risk (OARs) sparing. However, in APMCO plans, relatively remarkable decreases were observed in the mean dose (D_mean) to the glottic larynx and pharyngeal constrictor muscles. The reductions of average D_mean to the two OARs were 10.5% (p < 0.0001) and 8.4% (p < 0.0001), respectively. APMCO technique was found to increase the planning time for an average of approximately 5 h and did not lead to a significant increase of monitor units compared to general MCO.

CONCLUSIONS

The potential of the APMCO strategy is best realized with a clinical implementation that exploits individual generation of Pareto surface representations without manual interaction. It also assists physicians to ensure navigation in a more efficient and straightforward manner.

RayStation: External beam treatment planning system

RaySearch Laboratories is a world leader in the field of advanced software and creator of the RayStation treatment planning system for radiation therapy. The aim with RayStation is to deliver an unmatched user experience and leading functionality. Unique features described here include multiatlas based autosegmentation for contouring, deformable registration with 2 different algorithms, multicriteria optimization, Plan Explorer, fallback planning, ultrafast computation speed, and 4-dimensional (4D) adaptive radiation therapy. RayStation can be used to plan for electrons and photons on traditional linacs, for protons on various delivery systems, and for Accuray's helical TomoTherapy system. This paper describes some of these modalities, with reference to clinical cases and including descriptions of the impact on workflow.

Monaco treatment planning system tools and optimization processes

The Monaco treatment planning system combines Monte Carlo dose calculation accuracy with robust optimization tools to provide high-quality radiotherapy treatment plans for three-dimensional conformal radiotherapy (3D CRT), intensity modulated radiotherapy (IMRT), volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), and stereotactic body radiotherapy (SBRT). Recent technology advances have allowed for fast calculation speeds, which allow clinicians and patients to benefit from the accuracy of the Monte Carlo algorithm while reducing overall planning time. A collection of biological and physical dose-based planning tools and templates simplify the planning process and allow for consistent results across organizations. At the same time, multicriteria optimization (MCO) ensures critical organs are spared to the greatest possible degree while maintaining target coverage. Monaco encompasses a full suite of treatment modalities, including conventional radiotherapy and particle therapy, and is paving the way for real-time adaptive treatments with developments in magnetic resonance (MR)-guided radiation therapy.

Automated volumetric modulated arc therapy planning for whole pelvic prostate radiotherapy

Background

For several tumor entities, automated treatment planning has improved plan quality and planning efficiency, and may enable adaptive treatment approaches. Whole-pelvic prostate radiotherapy (WPRT) involves large concave target volumes, which present a challenge for volumetric arc therapy (VMAT) optimization. This study evaluates automated VMAT planning for WPRT-VMAT and compares the results with manual expert planning.

Methods

A system for fully automated multi-criterial plan generation was configured for each step of sequential-boost WPRT-VMAT, with final “autoVMAT” plans being automatically calculated by the Monaco treatment planning system (TPS; Elekta AB, Stockholm, Sweden). Configuration was based on manually generated VMAT plans (manualVMAT) of 5 test patients, the planning protocol, and discussions with the treating physician on wishes for plan improvements. AutoVMAT plans were then generated for another 30 evaluation patients and compared to manualVMAT plans. For all 35 patients, manualVMAT plans were optimized by expert planners using the Monaco TPS.

Results

AutoVMAT plans exhibited strongly improved organ sparing and higher conformity compared to manualVMAT. On average, mean doses (D_mean) of bladder and rectum were reduced by 10.7 and 4.5 Gy, respectively, by autoVMAT. Prostate target coverage (V_95%) was slightly higher (+0.6%) with manualVMAT. In a blinded scoring session, the radiation oncologist preferred autoVMAT plans to manualVMAT plans for 27/30 patients. All treatment plans were considered clinically acceptable. The workload per patient was reduced by > 70 min.

Conclusion

Automated VMAT planning for complex WPRT dose distributions is feasible and creates treatment plans that are generally dosimetrically superior to manually optimized plans.

Electronic supplementary material

The online version of this article (10.1007/s00066-017-1246-2) contains supplementary material, which is available to authorized users.

Multicriteria plan optimization in the hands of physicians: a pilot study in prostate cancer and brain tumors

Background

The purpose of this study was to demonstrate the feasibility of physician driven planning in intensity modulated radiotherapy (IMRT) with a multicriteria optimization (MCO) treatment planning system and template based plan optimization. Exploiting the full planning potential of MCO navigation, this alternative planning approach intends to improve planning efficiency and individual plan quality.

Methods

Planning was retrospectively performed on 12 brain tumor and 10 post-prostatectomy prostate patients previously treated with MCO-IMRT. For each patient, physicians were provided with a template-based generated Pareto surface of optimal plans to navigate, using the beam angles from the original clinical plans. We compared physician generated plans to clinically delivered plans (created by dosimetrists) in terms of dosimetric differences, physician preferences and planning times.

Results

Plan qualities were similar, however physician generated and clinical plans differed in the prioritization of clinical goals. Physician derived prostate plans showed significantly better sparing of the high dose rectum and bladder regions (p(D1) < 0.05; D1: dose received by 1% of the corresponding structure). Physicians’ brain tumor plans indicated higher doses for targets and brainstem (p(D1) < 0.05). Within blinded plan comparisons physicians preferred the clinical plans more often (brain: 6:3 out of 12, prostate: 2:6 out of 10) (not statistically significant). While times of physician involvement were comparable for prostate planning, the new workflow reduced the average involved time for brain cases by 30%. Planner times were reduced for all cases. Subjective benefits, such as a better understanding of planning situations, were observed by clinicians through the insight into plan optimization and experiencing dosimetric trade-offs.

Conclusions

We introduce physician driven planning with MCO for brain and prostate tumors as a feasible planning workflow. The proposed approach standardizes the planning process by utilizing site specific templates and integrates physicians more tightly into treatment planning. Physicians’ navigated plan qualities were comparable to the clinical plans. Given the reduction of planning time of the planner and the equal or lower planning time of physicians, this approach has the potential to improve departmental efficiencies.

Advanced optimization methods for whole pelvic and local prostate external beam therapy

PURPOSE

Radiation treatment planning inherently involves multiple conflicting planning goals, which makes it a suitable application for multicriteria optimization (MCO). This study investigates a MCO algorithm for VMAT planning (VMAT-MCO) for prostate cancer treatments including pelvic lymph nodes and uses standard inverse VMAT optimization (sVMAT) and Tomotherapy planning as benchmarks.

METHODS

For each of ten prostate cancer patients, a two stage plan was generated, consisting of a stage 1 plan delivering 22Gy to the prostate, and a stage 2 plan delivering 50.4Gy to the lymph nodes and 56Gy to the prostate with a simultaneous integrated boost. The single plans were generated by three planning techniques (VMAT-MCO, sVMAT, Tomotherapy) and subsequently compared with respect to plan quality and planning time efficiency.

RESULTS

Plan quality was similar for all techniques, but sVMAT showed slightly better rectum (on average Dmean -7%) and bowel sparing (Dmean -17%) compared to VMAT-MCO in the whole pelvic treatments. Tomotherapy plans exhibited higher bladder dose (Dmean +42%) in stage 1 and lower rectum dose (Dmean -6%) in stage 2 than VMAT-MCO. Compared to manual planning, the planning time with MCO was reduced up to 12 and 38min for stage 1 and 2 plans, respectively.

CONCLUSION

MCO can generate highly conformal prostate VMAT plans with minimal workload in the settings of prostate-only treatments and prostate plus lymph nodes irradiation. In the whole pelvic plan manual VMAT optimization led to slightly improved OAR sparing over VMAT-MCO, whereas for the primary prostate treatment plan quality was equal.

Microalgae-based biodiesel: a multicriteria analysis of the production process using realistic scenarios

Microalgae-based biodiesel has several benefits over other resources such as less land use, potential cultivation in non-fertile locations, faster growth and especially a high lipid-to-biodiesel yield. Nevertheless, the environmental and economic behavior for high scale production depends on several variables that must be addressed in the scale-up procedure. In this sense, rigorous modeling and multicriteria evaluation are performed in order to achieve optimal topology for third generation biodiesel production. Different scenarios and the most promising technologies tested at pilot scale are assessed. Besides, the sensitivity analysis allows the detection of key operating variables and assumptions that have a direct effect on the lipid content. The deviation of these variables may lead to an erroneous estimation of the scale-up performance of the technology reviewed in the microalgae-based biodiesel process. The modeling and evaluation of different scenarios of the harvesting, oil extraction and transesterification help to identify greener and cheaper alternatives.

Maximizing dosimetric benefits of IMRT in the treatment of localized prostate cancer through multicriteria optimization planning

We examine the quality of plans created using multicriteria optimization (MCO) treatment planning in intensity-modulated radiation therapy (IMRT) in treatment of localized prostate cancer. Nine random cases of patients receiving IMRT to the prostate were selected. Each case was associated with a clinically approved plan created using Corvus. The cases were replanned using MCO-based planning in RayStation. Dose-volume histogram data from both planning systems were presented to 2 radiation oncologists in a blinded evaluation, and were compared at a number of dose-volume points. Both physicians rated all 9 MCO plans as superior to the clinically approved plans (p<10(-5)). Target coverage was equivalent (p = 0.81). Maximum doses to the prostate and bladder and the V50 and V70 to the anterior rectum were reduced in all MCO plans (p<0.05). Treatment planning time with MCO took approximately 60 minutes per case. MCO-based planning for prostate IMRT is efficient and produces high-quality plans with good target homogeneity and sparing of the anterior rectum, bladder, and femoral heads, without sacrificing target coverage.