The process of identifying the optimal unknown variables for the creation of a precision fuel-cell performance forecasting model using optimization techniques is known as parameter identification of the proton exchange membrane fuel cell (PEMFC). Recognizing these factors is crucial for accurately forecasting and assessing the fuel cell's performance, as they may not always be included in the manufacturer's datasheet. Six optimization algorithms—the Walrus Optimizer (WO), the Tunicate Swarm Algorithm (TSA), the Harris Hawks Optimizer (HHO), the Heap Based Optimizer (HBO), the Chimp Optimization Algorithm (ChOA), and the Osprey Optimization Algorithm (OOA) were used to compute six unknown variables of a PEMFC. Also, the proposed WO method was compared with other published works' methods such as the Equilibrium Optimizer (EO), Manta Rays Foraging Optimizer (MRFO), Neural Network Algorithm (NNA), Artificial Ecosystem Optimizer (AEO), Slap Swarm Optimizer (SSO), and Vortex Search Approach with Differential Evolution (VSDE). Minimizing the sum squares error (SSE) between the estimated and measured cell voltages requires treating these six parameters as choice variables during optimization. The WO algorithm yielded an SSE of 1.945415603, followed by HBO, HHO, TSA, ChOA, and OOA. Given that WO accurately forecasted the fuel cell's performance, it is appropriate for the development of digital twins for fuel cell applications and control systems for the automobile industry. Furthermore, it was shown that the WO convergence speed was faster than the other approaches studied.
Citation: Essam H. Houssein, Nagwan Abdel Samee, Maali Alabdulhafith, Mokhtar Said. Extraction of PEM fuel cell parameters using Walrus Optimizer[J]. AIMS Mathematics, 2024, 9(5): 12726-12750. doi: 10.3934/math.2024622
[1] | Gurninder Singh Sandhu . On an identity involving generalized derivations and Lie ideals of prime rings. AIMS Mathematics, 2020, 5(4): 3472-3479. doi: 10.3934/math.2020225 |
[2] | Shakir Ali, Amal S. Alali, Sharifah K. Said Husain, Vaishali Varshney . Symmetric n-derivations on prime ideals with applications. AIMS Mathematics, 2023, 8(11): 27573-27588. doi: 10.3934/math.20231410 |
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[5] | Gurninder S. Sandhu, Deepak Kumar . A note on derivations and Jordan ideals of prime rings. AIMS Mathematics, 2017, 2(4): 580-585. doi: 10.3934/Math.2017.4.580 |
[6] | Ali Yahya Hummdi, Amr Elrawy . On neutrosophic ideals and prime ideals in rings. AIMS Mathematics, 2024, 9(9): 24762-24775. doi: 10.3934/math.20241205 |
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[8] | Gurninder S. Sandhu, Deepak Kumar . Correction: A note on derivations and Jordan ideals in prime rings. AIMS Mathematics, 2019, 4(3): 684-685. doi: 10.3934/math.2019.3.684 |
[9] | Jayanta Ghosh, Dhananjoy Mandal, Tapas Kumar Samanta . Soft prime and semiprime int-ideals of a ring. AIMS Mathematics, 2020, 5(1): 732-745. doi: 10.3934/math.2020050 |
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The process of identifying the optimal unknown variables for the creation of a precision fuel-cell performance forecasting model using optimization techniques is known as parameter identification of the proton exchange membrane fuel cell (PEMFC). Recognizing these factors is crucial for accurately forecasting and assessing the fuel cell's performance, as they may not always be included in the manufacturer's datasheet. Six optimization algorithms—the Walrus Optimizer (WO), the Tunicate Swarm Algorithm (TSA), the Harris Hawks Optimizer (HHO), the Heap Based Optimizer (HBO), the Chimp Optimization Algorithm (ChOA), and the Osprey Optimization Algorithm (OOA) were used to compute six unknown variables of a PEMFC. Also, the proposed WO method was compared with other published works' methods such as the Equilibrium Optimizer (EO), Manta Rays Foraging Optimizer (MRFO), Neural Network Algorithm (NNA), Artificial Ecosystem Optimizer (AEO), Slap Swarm Optimizer (SSO), and Vortex Search Approach with Differential Evolution (VSDE). Minimizing the sum squares error (SSE) between the estimated and measured cell voltages requires treating these six parameters as choice variables during optimization. The WO algorithm yielded an SSE of 1.945415603, followed by HBO, HHO, TSA, ChOA, and OOA. Given that WO accurately forecasted the fuel cell's performance, it is appropriate for the development of digital twins for fuel cell applications and control systems for the automobile industry. Furthermore, it was shown that the WO convergence speed was faster than the other approaches studied.
A ring R is considered prime if, for any elements π and ξ in R, the condition πRξ=0 implies that either π=0 or ξ=0. In our discussion, unless otherwise stated, R refers to a prime ring with its center denoted by Z(R), and Qr refers to its right Martindale quotient ring. Notably, Qr retains the prime property of R. Additionally, the center of Qr, known as the extended centroid of R, is a field.
To simplify the notation, we use [π,ξ]=πξ−ξπ for all π,ξ∈R. A subset L of R is called a Lie ideal of R if it forms an additive subgroup and satisfies the condition that the commutator of L with any element of R remains within L, i.e., [L,R]⊆L.
Definition 1.1. [3] A mapping d:R→R is called a derivation if it is additive and
d(πξ)=d(π)ξ+πd(ξ),for allπ,ξ∈R. |
For a fixed v∈R, the mapping dv:R→R defined as dv(π)=[v,π] for all π∈R is a derivation termed as an inner derivation induced by the element v. A derivation that is not inner is referred to as an outer derivation. In 1957, Posner [4] showed that if R is a prime ring and d is a nontrivial derivation of R such that [d(π),π]∈Z(R) for all π∈R, then R is commutative. Posner's results were later extended in various ways by other mathematicians.
In 1991, M. Breˇsar [5] proposed a new kind of derivation, known as a generalized derivation.
Definition 1.2. [5] A mapping Δ:R→R is said to be a generalized derivation if Δ is additive and there exists a derivation δ on R such that
Δ(πξ)=Δ(π)ξ+πδ(ξ),for allπ,ξ∈R. |
For fixed elements v1,v2∈R, the mapping Δ(v1,v2):R→R defined by Δ(v1,v2)(π)=v1π+πv2 is a generalized derivation on R, often referred to as a generalized inner derivation.
Definition 1.3. [3] A mapping D:R→R is called a skew derivation associated with the automorphism α∈Aut(R) if it is additive and it satisfies
D(πξ)=D(π)ξ+α(π)D(ξ),for all π,ξ∈R. |
A skew derivation that is associated with the identity automorphism reduces to a derivation. For example, given a fixed element b in Qr, the mapping defined by π↦bπ−α(π)b is a notable example of a skew derivation, commonly known as an inner skew derivation. If a skew derivation does not fit this structure, it is termed an outer skew derivation.
Definition 1.4. [3] A mapping ϕ:R→R is called a generalized skew derivation associated with the automorphism α∈Aut(R) if it is additive and there exists a skew derivation δ on R such that
ϕ(πξ)=ϕ(π)ξ+α(π)δ(ξ),for all π,ξ∈R. |
In 2021, De Filippis [6] studied the identity Δ1(Δ2(π))=0 for all π∈L, where Δ1 and Δ2 are generalized skew derivations on a prime ring R, with L being a Lie ideal of R. This identity was examined within the framework of generalized derivations.
In 2018, De Filippis and Wei [7] developed the notion of b-generalized skew derivation, which broadens the concept of derivations and investigates different kinds of linear mappings in noncommutative algebras.
Definition 1.5. [7] Let b be a fixed element in the right Martindale quotient ring Qr. The mapping Δ1:R→Qr is called a b-generalized skew derivation of R associated with the triplet (b,α,d) if it is additive and it satisfies the condition
Δ1(πξ)=Δ1(π)ξ+bα(π)d(ξ) |
for all π,ξ∈R, where d:R→Qr is an additive mapping and α is an automorphism of R.
Furthermore, the authors showed that when b≠0, the corresponding additive map d, as defined earlier, acts as a skew derivation. Additionally, it has been established that the additive mapping Δ1 can be extended to the right Martindale quotient ring Qr, taking the form Δ1(π)=aπ+bd(π), where a∈Qr. The concept of b-generalized skew derivation, characterized by the triplet (b,α,d), includes skew derivations, generalized derivations, and left multipliers, among other concepts. For instance, setting b=1 yields a skew derivation, while choosing b=1 and α=IR results in a generalized derivation, with IR representing the identity map on R. Additionally, if b=0 in Definition 1.5, then Δ1 reduces to a left multiplier map. The mapping Δ1:R→Qr, given by π↦aπ+bα(π)c is a notable example of b-generalized skew derivation of R associated with the triplet (b,v1,d), where a, b,c∈Qr, and d(π)=α(π)c−cπ for all π∈R. This type of b-generalized skew derivation is known as an inner b-generalized skew derivation. Therefore, the study of b-generalized skew derivations of a ring R provides insights into the study of other types of derivations.
These broad results concerning b-generalized skew derivations lead to significant corollaries related to derivations, generalized derivations, and generalized skew derivations. Such findings offer valuable insights for applications and further advancements in the study of these related concepts.
It is quite natural to examine the implications of substituting derivations with b-generalized skew derivations in the results originally obtained by Posner and Breˇsar. In 2021, Filippis et al. [8] made progress in extending Breˇsar's result by investigating the identity Δ1(π)π−πΔ2(π)=0 involving b-generalized skew derivations Δ1 and Δ2 in a prime ring R. Here, π represents elements of the form ϕ(π1,…,πn), where π=(π1,…,πn)∈Rn, and ϕ(π) is a multi-linear polynomial over C. Relevant generalizations related to b-generalized skew derivations can be found in [3,7,8,9,10,11,12].
Continuing the investigation of above cited results, we focus to study the following identity pπΔ1(π)+Δ1(π)πq=Δ2(π2), where p+q∉C for all π∈L. The primary motivation for this identity comes from the articles [1] and [2]. In [1], the authors examined the identity pπF(π)+F(π)πq=G(π2), where F, G are derivations and π∈S, a particular subset of R. In [2], the same identity was explored with F and G considered as generalized derivations. Naturally, it is of interest to investigate this identity further by taking F and G as b-generalized skew derivations. The following theorem establishes our result:
Theorem 1.6. Let R be a prime ring with characteristic different from 2, Qr its right Martindale quotient ring, C its extended centroid, and L a noncentral Lie ideal of R. Suppose Δ1 and Δ2 are non-zero b-generalized skew derivations of R with associated triples (b,α,d) and (b,α,h), respectively, satisfying the identity:
pπΔ1(π)+Δ1(π)πq=Δ2(π2)for somep,q∈Rwithp+q∉C, ∀π∈L. |
Then, for all π∈R, one of the following holds:
1) There exist a∈C and c∈Qr such that Δ1(π)=aπ, Δ2(π)=πc with pa=c−aq∈C.
2) There exist a∈C and c,c′∈Qr such that Δ1(π)=aπ, Δ2(π)=cπ+πc′ with pa−c∈C and (p+q)a=c+c′.
3) There exist a,q∈C and c∈Qr such that Δ1(π)=aπ, Δ2(π)=cπ with (p+q)a=c.
4) There exist a,b,u,v,t∈Qr, and λ,η∈C such that Δ1(π)=(a+bπ)π, Δ2(π)=cπ+btπt−1v with t−1v+ηq=λ∈C, (a+bπ)+ηbt=0, and p(a+bπ)−c=λbt.
5) R satisfies s4.
The standard polynomial identity s4 in four variables is defined as follows:
s4(π1,π2,π3,π4)=∑σ∈Sym(4)(−1)σπσ(1)πσ(2)πσ(3)πσ(4), |
where (−1)σ is +1 or −1 depending on whether σ represents an even or odd permutation in the symmetric group Sym(4).
The general approach for proving the main theorem can be extended to demonstrate a broader result for multi-linear polynomials. Consequently, from a theoretical standpoint, there is no distinction between cases involving Lie ideals and those involving multi-linear polynomials. Applying the proof method suitable for multi-linear polynomials streamlines the process by minimizing excessive calculations. The paper is structured as follows: Section 2 provides a review of fundamental concepts regarding prime rings. Section 3 explores the case where the b-generalized skew derivations Δ1 and Δ2 are inner. In Section 4, we establish our main theorem by carefully examining each case.
We frequently utilize the following facts to establish our results:
Fact 2.1. [13] Let R be a prime ring and I a two-sided ideal of R. Then, R, I, and Qr satisfy the same generalized polynomial identities with coefficients in Qr.
Fact 2.2. [14] Let R be a prime ring and I a two-sided ideal of R. Then, R,I, and Qr satisfy the same differential identities.
Fact 2.3. [13] Let R be a prime ring. Then, every derivation d of R can be uniquely extended to a derivation of Qr.
Fact 2.4. [15, Chuang] Let R be a prime ring, d be a nonzero skew derivation on R, and I a nonzero ideal of R. If I satisfies the differential identity.
f(ζ1,ζ2,…,ζn,d(ζ1),d(ζ2)…,d(ζn))=0 |
for any ζ1,…,zetan∈I, then either
● I satisfies the generalized polynomial identity,
f(ζ1,ζ2…,ζn,ξ1,xi2…,xin)=0 |
for all ξ1,…,ξn∈R.
or
● d is Qr-inner,
f(ζ1,ζ2,…,ζn,[p,ζ1],[p,ζ2]…,[p,ζn])=0. |
Fact 2.5. [16] Let K be an infinite field and m≥2 an integer. If P1,…,Pk are non-scalar matrices in Mm(K), then there exists some invertible matrix P∈Mm(K) such that each matrix PP1P−1,…,PPkP−1 has all nonzero entries.
Fact 2.6. [17] Let R be a noncommutative prime ring of characteristic not equal to 2 with right Martindale quotient ring Qr and extended centroid C, and let f(ζ1,…,ζn) be a multi-linear polynomial over C, which is not central valued on R. Suppose that there exists a,b,c∈Qr such that f(χ)af(χ)+f(χ)2b−cf(χ)2=0 for all χ=(ζ1,…,ζn)∈R. Then, one of the following holds:
1) b,c∈C, C−b=a=α∈C.
2) f(ζ1,…,ζn)2 is central valued and there exists α∈C such that c−b=a=α.
Fact 2.7. [7] If d is a nonzero skew derivation on a prime ring R, then associated automorphism α is unique.
Fact 2.8. [6] Let R be a prime ring, ϕ,γ be two automorphisms of Qr and d,g be two skew derivations on R associated with the same automorphism ϕ. If there exist a nonzero central element ν and v∈Qr such that
G(ζ)=(vζ−γ(ζ)v)+νd(ζ),for allζ∈R. |
then, G(ζ)=νd(ζ) and one of the following holds:
1) ϕ=γ.
2) v=0.
Fact 2.9. [6] Let R be a prime ring, ϕ,γ be two automorphisms of Qr, and d,g be two skew derivations on R associated with the same automorphism ϕ. If there exist a nonzero central element ν and v∈Qr such that
G(ζ)=(vζ−γ(ζ)v)+νd(ζ),for allζ∈R. |
If d is inner skew derivation, then so is G.
In this paper, R will consistently refer to a nontrivial, associative prime ring (unless specified otherwise). Additionally, the term "GPI" will be used as a shorthand for generalized polynomial identity.
In this section, we focus on the case where Δ1 and Δ2 are inner b-generalized skew derivations of R associated with the pair (b,α). More specifically, we investigate Theorem 1.6 under the conditions Δ1(π)=aπ+bα(π)u and Δ2(π)=cπ+bα(π)v for all π∈R, where a,b,c,u,v∈Qr. To establish the main result, we first present the following lemmas:
Lemma 3.1. Let R be a prime ring of char(R)≠2 and a1, a2, a3, a4, and a5∈R such that
a1u2+a2u2a3+a4u2a5=0, ∀ u=[π1,π2]∈[R,R]. | (3.1) |
Then, one of the following holds:
1) R satisfies s4.
2) a3,a4∈C, and a1+a2a3=−a5a4∈C.
3) a3,a5∈C, and a1+a2a3+a5a4=0.
4) a1,a2,a4∈C, and a1+a2a3+a5a4=0.
5) a2,a5∈C, and a1+a4a5=−a2a3∈C.
6) There exist λ, η, μ∈C such that a5+ηa3=λ, a2−ηa4=μ, and a1+λa4=−μa3∈C
Proof. If u2 is a centrally valued element in R, then R satisfies the identity s4, which leads to our first conclusion. Now, suppose u2 is not central. Let S be the additive subgroup of R generated by the set {u2:u∈[R,R]}. Clearly, S≠0, and we have the relation:
a1π+a2πa3+a4πa5=0 |
for all π∈S.
According to [18], either S⊆Z(R), or char(R)=2 and R satisfies s4, unless S contains a non-central ideal L′ of R. Since u2 is not centrally valued in R, the first possibility is excluded. Additionally, since char(R)≠2, it follows that S contains a noncentral Lie ideal L′ of R. By [19], there exists a noncentral two-sided ideal I of R such that [I,R]⊆L′. Under the given hypothesis, we have
a1[π1,π2]+a2[π1,π2]a3+a4[π1,π2]a5=0 |
for all π1,π2∈I. From Fact 2.1, since Qr, I, and R satisfy the same GPI, it follows that
a1[π1,π2]+a2[π1,π2]a3+a4[π1,π2]a5=0 |
for all π1,π2∈R. Therefore, by [[20], Proposition 2.13], we obtain the desired conclusions.
Lemma 3.2. Let R=Mm(C), where m≥2, be the ring of all m×m matrices over an infinite field C with characteristic not equal to 2. Suppose a,b,c,u,v,p,q∈R satisfy:
pΠaΠ+pΠbΠu+aΠ2q+bΠuΠq−cΠ2−bΠ2v=0 |
for all Π=[π1,π2]∈[R,R]. Then, either b∈C, u∈C, or p+q∈C.
Proof. Assume the field C is infinite. From the hypothesis:
pΠaΠ+pΠbΠu+aΠ2q+bΠuΠq−cΠ2−bΠ2v=0 | (3.2) |
for all Π∈[R,R]. If we assume that p+q, b, and u are not central elements, and since Eq (3.2) holds invariantly under any automorphism of R (as stated in Fact 2.5), it implies that all entries of p+q, b, and u are nonzero. By selecting Π=eij in Eq (3.2), we obtain:
peijaeij+peijbeiju+beijueijq=0. | (3.3) |
Next, multiplying Eq (3.3) both on the right and the left by eij gives:
(p+q)jiujibjieij=0, |
which implies that either (p+q)ji=0, uji=0, or bji=0. Each of these scenarios leads to a contradiction. Therefore, it follows that either p+q∈C, or b∈C, or u∈C.
Lemma 3.3. Let R=Mm(C), where m≥2, be the ring of all m×m matrices over a field C with characteristic not equal to 2. Suppose a,b,c,u,v,p,q∈R satisfy:
pΠaΠ+pΠbΠu+aΠ2q+bΠuΠq−cΠ2−bΠ2v=0 |
for all Π=[π1,π2]∈[R,R]. Then, either b∈C, u∈C, or p+q∈C.
Proof. If C is an infinite field, the conclusion follows directly from Lemma 3.2. Now, let's consider the case where the field C is finite. Let K be an infinite extension field of C, and set ˉR=Mm(K)≅R⊗CK. It is important to note that a multi-linear polynomial is central-valued on R if and only if it is central-valued on ˉR.
Consider the generalized polynomial identity for R given by
Q(π1,π2)=p[π1,π2]a[π1,π2]+p[π1,π2]b[π1,π2]u+a[π1,π2]2q+b[π1,π2]u[π1,π2]q−c[π1,π2]2−b[π1,π2]2v. | (3.4) |
This polynomial has a multi-degree of (2,2) with respect to the indeterminates π1 and π2. Therefore, the complete linearization of Q(π1,π2) results in a multi-linear generalized polynomial Θ(π1,π2,ξ1,ξ2) involving four indeterminates. Additionally, we have the relation Θ(π1,π2,π1,π2)=4Q(π1,π2).
It is clear that the multi-linear polynomial Θ(π1,π2,ξ1,ξ2) serves as a generalized polynomial identity for both R and ˉR. Given that the characteristic of R is not equal to 2, as per the assumption, we conclude that Q(π1,π2)=0 for all π1,π2∈ˉR. Hence, the result follows from Lemma 3.2.
Lemma 3.4. Let R be a prime ring of characteristic different from 2, with Martindale quotient ring Qr and extended centroid C. Suppose that for some a,b,c,u,v,p,q∈R, the following holds:
pΠaΠ+pΠbΠu+aΠ2q+bΠuΠq−cΠ2−bΠ2v=0 |
for all Π=[π1,π2]∈[R,R]. Then, either b∈C, u∈C, or p+q∈C.
Proof. Case 1: Suppose none of b, u, or p+q is central. Given the hypothesis, we have
h(π1,π2)=p[π1,π2]a[π1,π2]+p[π1,π2]b[π1,π2]u+a[π1,π2]2q+b[π1,π2]u[π1,π2]q−c[π1,π2]2−b[π1,π2]2v | (3.5) |
for all π1,π2∈R. Define D=Qr⋆CC{π1,π2}, the free product of Qr and the free C-algebra C{π1,π2} in non-commuting indeterminates π1 and π2. Since both R and Qr satisfy the same GPI (from Facts 2.1 and 2.2), Qr satisfies h(π1,π2)=0 in D.
Now, let's treat h(π1,π2) as a trivial GPI for R. Thus, h(π1,π2) is a zero element in D. However, since b, u, and p+q are assumed not to be central, it must be that either b[π1,π2]u[π1,π2]q or p[π1,π2]b[π1,π2]u appears nontrivially in h(π1,π2), leading to a contradiction.
Hence, at least one of b, u, or p+q belongs to C.
Case 2: Now, suppose that h(π1,π2) is a nontrivial GPI for Qr. If C is infinite, then h(π1,π2)=0 for all π1,π2∈Qr⊗CˉC, where ˉC is the algebraic closure of C. Since Qr and Qr⊗CˉC are both prime and centrally closed (refer to Theorems 2.5 and 3.5 in [21]), we can replace R by either Qr or Qr⊗CˉC, depending on whether C is finite or infinite. Thus, R is centrally closed over C, and h(π1,π2)=0 for all π1,π2∈R.
By Martindale's theorem [22], R is a primitive ring with a nonzero socle, soc(R), and C as its associated division ring. By Jacobson's theorem (see p.75 in [23]), R is isomorphic to a dense ring of linear transformations on a vector space V over C.
Assuming first that V is finite-dimensional over C, i.e., dimCV=m, the density of R implies R≅Mm(C). Since R is noncommutative, therefore, m≥2. In this case, the result follows from Lemma 3.2.
Next, suppose V is infinite-dimensional over C. For any e2=e∈soc(R), we have eRe≅Mt(C) where t=dimCVe. Since none of b, u, or p+q is central, there exist h1,h2,h3∈soc(R) such that [b,h1]≠0, [u,h2]≠0, and [p+q,h3]≠0. By Litoff's theorem [24], there is an idempotent e∈soc(R) such that bh1,h1b,uh2,h2u,(p+q)h3,h3(p+q),h1,h2,h3∈eRe. Then, from Eq (3.5), we have:
e{p[eπ1e,eπ2e]a[eπ1e,eπ2e]+p[eπ1e,eπ2e]b[eπ1e,eπ2e]u+a[eπ1e,eπ2e]2q+b[eπ1e,eπ2e]u[eπ1e,eπ2e]q−c[eπ1e,eπ2e]2−b[eπ1e,eπ2e]2v}e=0 | (3.6) |
for all π1,π2∈R. The subring eRe satisfies:
epe[π1,π2]eae[π1,π2]+epe[π1,π2]ebe[π1,π2]eue+eae[π1,π2]2eqe+ebe[π1,π2]eue[π1,π2]eqe−ece[π1,π2]2−ebe[π1,π2]2eve=0 | (3.7) |
for all π1,π2∈R. By the finite-dimensional case above, either ebe, or eue, or e(p+q)e is a central element of eRe. Thus, one of the following must hold: bh1=(ebe)h1=h1ebe=h1b, or uh2=(eue)h2=h2(eue)=h2u, or (p+q)h3=e(p+q)eh3=h3(e(p+q)e)=h3(p+q), which contradicts the initial assumption.
Therefore, we conclude that either b∈C, or u∈C, or p+q∈C.
From the previous arguments, we can prove the following lemmas:
Lemma 3.5. Let R be a prime ring of characteristic different from 2 with Martindale quotient ring Qr and extended centroid C. Suppose that for some a,p,q∈R,
pΠaΠ+aΠ2q=0 |
for all Π=[π1,π2]∈[R,R]. Then, either a∈C or both p and pa∈C.
Lemma 3.6. Let R be a prime ring of characteristic different from 2 with Martindale quotient ring Qr and extended centroid C. Suppose that for some p,q∈R,
pΠ2+Π2q=0 |
for all Π=[π1,π2]∈[R,R]. Then, either R satisfies s4 or p+q=0.
Proposition 3.7. Let R be a prime ring of characteristic different from 2, Qr be its Martindale ring of quotients with extended centroid C, and L=[R,R] be a Lie ideal of R. Let Δ1,Δ2 be two b-generalized skew inner derivations of R with associated pair (b,α). Suppose there exist elements p,q∈R such that
pΠΔ1(Π)+Δ1(Π)Πq=Δ2(Π2), with p+q∉C, ∀Π∈L. |
Then, for all π∈R, one of the following holds:
1) There exist a∈C and c∈Qr such that Δ1(π)=aπ,Δ2(π)=πc with pa=c−aq∈C.
2) There exist a∈C and c,c′∈Qr such that Δ1(π)=aπ, Δ2(π)=cπ+πc′ with pa−c∈C and (p+q)a=c+c′.
3) There exist a,q∈C and c∈Qr such that Δ1(π)=aπ, Δ2(π)=cπ with (p+q)a=c.
4) There exist a,b,u,v,t∈Qr and λ,η∈C such that Δ1(π)=(a+bu)π, Δ2(π)=cπ+btπt−1v with t−1v+ηq=λ∈C, (a+bu)+ηbt=0, and p(a+bu)−c=λbt.
5) R satisfies s4.
Proof. From the hypothesis, we have:
pΠaΠ+pΠbα(Π)u+aΠ2q+bα(Π)uΠq−cΠ2−bα(Π2)v=0 | (3.8) |
for all Π=[π1,π2]∈[R,R].
Case 1: Suppose the associated automorphism α is inner, then there exists an invertible element t∈R such that α(π)=tπt−1 for all π∈R. Thus, Eq (3.8) becomes:
pΠaΠ+pΠbtΠt−1u+aΠ2q+btΠt−1uΠq−cΠ2−btΠ2t−1v=0 | (3.9) |
for all Π∈[R,R]. Then from Lemma 3.4, either bt∈C or t−1u∈C.
Sub-case (a): If bt∈C, then Eq (3.9) reduces to:
pΠaΠ+pΠ2bu+aΠ2q+ΠbuΠq−cΠ2−Π2bv=0 | (3.10) |
for all Π∈[R,R]. Again, by previous arguments, one of the following holds:
1) p,pa,bu∈C.
2) a,bu∈C.
3) a,q,buq∈C.
Now, we will discuss each of the above cases in detail.
1) Suppose pa,p,bu∈C, then Eq (3.10) reduces to:
(pa−c)Π2+aΠ2q+Π2(pbu+buq−bv)=0 | (3.11) |
for all Π=[π1,π2]∈[R,R]. Then, from Lemma 3.1, one of the following holds:
● R satisfies s4, which is our Conclusion (5).
● q∈C, which implies that p+q∈C, a contradiction.
● q,(pbu+buq−bv)∈C, which implies that p+q∈C, a contradiction.
● (pa−c),a∈C, which implies that c∈C. Thus, from Eq (3.11), we get (a+bu)p=(c+bv)−(a+bu)q. Hence, in this case, we get Δ1(π)=(a+bu)π and Δ2(π)=π(c+bv) for all π∈R with (a+bu)p=(c+bv)−(a+bu)q, which is our Conclusion (1).
● q,(pbu+buq−bv)∈C, which gives that p+q∈C, a contradiction.
● There exist η,λ,μ∈C such that (pbu+buq−bv)+ηq=λ, a−η=μ and (pa−c)+λ=−μq∈C. If μ≠0, then q∈C, which implies p+q∈C, a contradiction. If μ=0, then pa−c,a∈C; then by previous arguments, we get our Conclusion (1).
2) Suppose a,bu∈C. Then, Eq (3.10) reduces to:
(p(a+bu)−c)Π2+aΠ2q+Π2(buq−bv)=0 | (3.12) |
for all Π=[π1,π2]∈[R,R]. Then, from Lemma 3.1, one of the following holds:
● R satisfies s4, which is our Conclusion 5.
● q∈C and p(a+bu)−c+aq=−buq+bv∈C. Thus, in this case, we get our Conclusion (2).
● q,(buq−bv)∈C, which implies bv∈C. Thus, from Eq (3.12), we get (pa+pbu−c+aq+buq−bv)Π2=0, which implies (a+bu)q=(c+bv)−(a+bu)p∈C. Hence, in this case, we get Δ1(π)=(a+bu)π and Δ2(π)=(c+bv)π for all π∈R with (a+bu)q=(c+bv)−(a+bu)p∈C, which is our Conclusion (3).
● (p(a+bu)−c)∈C. Then, from Eq (3.12), we get Π2(pa+pbu−c+aq+buq−bv)=0, which implies (p+q)(a+bu)=(c+bv). Thus, in this case, we get Δ1(π)=(a+bu)π, Δ2(π)=cπ+πbv for all π∈R with (p+q)(a+bu)=(c+bv), which is our Conclusion (2).
● (buq−bv),a∈C, and p(a+bu)−c+buq−bv=−aq∈C. Since buq−bv∈C, we have p(a+bu)−c∈C. Thus, in this case, we get our Conclusion (2).
● There exist η,λ,μ∈C such that (buq−bv)+ηq=λ, a−η=μ, and (p(a+bu)−c)+λ=−μq∈C. If μ≠0, then q∈C, which implies bv∈C. Thus, by previous arguments, we get our Conclusion (3). If μ=0, then p(a+bu)−c∈C, and by previous arguments, we get our Conclusion (2).
3) Suppose a,q,buq∈C, then Eq (3.10) reduces to:
(ap+aq+buq−c)Π2+pΠ2bu−Π2bv=0 | (3.13) |
for all Π∈[R,R]. Then, from Lemma 3.1, one of the following holds:
● R satisfies s4, which is our Conclusion (5).
● bu∈C and ap+aq+buq−c+pbu=bv∈C. Therefore, we have p(a+bu)−c∈C. Also, we have (a+bu)q∈C. If a+bu=0, then c+bv=0, and we get our conclusion (1). If a+bu≠0, then we get q∈C; thus, in this situation, we get Conclusion (3).
● bu,bv∈C, and our functions take the form Δ1(π)=(a+bu)π, Δ2(π)=(c+bv)π for all π∈R with (a+bu)q=(c+bv)−(a+bu)p, which is our Conclusion (3).
● (ap+aq+buq−c),p∈C; this gives p+q∈C, a contradiction.
● p,bv∈C; this gives p+q∈C, a contradiction.
● There exist η,λ,μ∈C such that −bv+ηbu=λ, p−η=μ, and (ap+aq+buq−c)+λ=−μbu∈C. This gives that p=η+μ∈C, and, hence, p+q∈C, a contradiction.
Sub-case (b): If t−1u∈C, then Eq (3.9) reduces to:
pΠaΠ+pΠbuΠ+aΠ2q+buΠ2q−cΠ2−btΠ2t−1v=0 | (3.14) |
for all Π∈[R,R]. Again, by previous arguments, one of the following holds:
a) p,p(a+bu)∈C,
b) a+bu∈C.
a) Now, if p,p(a+bu)∈C, then Eq (3.14) reduces to the following:
(p(a+bu)−c)Π2+(a+bu)Π2q−btΠ2t−1v=0. | (3.15) |
Now, by Lemma 3.1, one of the following holds:
● R satisfies s4, which is our Conclusion (5).
● q,bt∈C, which implies p+q∈C, a contradiction.
● t−1v,q∈C, which implies p+q∈C, a contradiction.
● p(a+bu)−c,(a+bu),bt∈C with p(a+bu)−c+(a+bu)q=bv, which implies c∈C and p(a+bu)=−q(a+bu)+(c+bv)∈C. Thus, in this situation, we get Δ1(π)=(a+bu)π and Δ2(π)=π(c+bv) for all π∈R, which is our Conclusion (1).
● (a+bu),t−1v∈C with p(a+bu)−c−bv=−(a+bu)q∈C. If a+bu=0, then c+bv=0 and, thus, we get our Conclusion (1). Now, if a+bu≠0, then q∈C. Therefore, p+q∈C, which is a contradiction.
● There exist η,λ,μ∈C such that t−1v+ηq=λ∈C, (a+bu)+ηbt=μ∈C, and p(a+bu)−c−λbt=−μq∈C. If μ≠0, then q∈C and, thus, p+q∈C, a contradiction.
Again, if μ=0, then t−1v+ηq=λ∈C, (a+bu)+ηbt=0, and p(a+bu)−c=λbt. Thus, in this situation, we get our Conclusion (4).
b) Now, if (a+bu)∈C, then Eq (3.14) transforms into the following:
(p(a+bu)−c)Π2−btΠ2t−1v+Π2(a+bu)q=0 | (3.16) |
for all Π∈[R,R]. By Lemma 3.1, one of the following holds:
● R satisfies s4, which is our Conclusion (5).
● t−1v∈C and (p(a+bu)−c)−bv=−(a+bu)q∈C. In this situation, we get Δ1(π)=(a+bu)π and Δ2(π)=(c+bv)π for all π∈R with q(a+bu)=(c+bv)−p(a+bu). This is our Conclusion (2).
● t−1v,(a+bu)q∈C, and p(a+bu)+(a+bu)q−c−bv=0. In this situation, we get our conclusion from previous arguments.
● (p(a+bu)−c),bt∈C, and p(a+bu)+(a+bu)q−c−bv=0. The functions Δ1 and Δ2 take the form Δ1(π)=(a+bu)π, Δ2(π)=cπ+πbv for all π∈R with (p+q)(a+bu)=(c+bv). This is our Conclusion (2).
● bt,(a+bu)q,t−1v∈C, and p(a+bu)−c+(a+bu)q=bv∈C. Thus, the functions Δ1 and Δ2 take the form Δ1(π)=(a+bu)π, Δ2(π)=(c+bv)π for all π∈R with q(a+bu)=(c+bv)−(a+bu)p. This is our Conclusion (2).
● There exist η,λ,μ∈C such that (a+bu)q+ηt−1v=λ, −bt−η=μ, and (p(a+bu)−c)+λ=−μt−1v∈C. If μ≠0, then q,t−1v∈C. Thus, from Eq (3.16), we get (p+q)(a+bu)=(c+bv). Hence, in this situation, the functions Δ1 and Δ2 take the form Δ1(π)=(a+bu)π, Δ2(π)=(c+bv)π for all π∈R. This is our Conclusion (3).
Now, if μ=0, then bt,p(a+bu)−c∈C. Then, by previous arguments, we get our Conclusion (2).
Case 2: Since R and Qr satisfy the same differential polynomial identities with coefficients in Qr (see Fact 2.2), it follows from Eq (3.8) that:
pΠaΠ+pΠbα(Π)u+aΠ2q+bα(Π)uΠq−cΠ2−bα(Π2)v=0, | (3.17) |
for all Π=[π1,π2]∈[Qr,Qr]. If α is an outer derivation, then by Fact 2.4, we have:
p[π1,π2]a[π1,π2]+p[π1,π2]b[ξ1,ξ2]u+a[π1,π2]2q+b[ξ1,ξ2]u[π1,π2]q−c[π1,π2]2−b[ξ1,ξ2]2v=0, | (3.18) |
for all π1,π2,ξ1,ξ2∈R. In particular, Qr satisfies b[ξ1,ξ2]2v=0, which implies either b=0 or v=0.
If b=0, then Eq (3.18) simplifies to:
p[π1,π2]a[π1,π2]+a[π1,π2]2q−c[π1,π2]2=0, | (3.19) |
for all π1,π2∈R. Thus, from Sub-case (b) of Case 1, we reach our conclusions. Now, if v=0, then Eq (3.18) reduces to:
p[π1,π2]a[π1,π2]+p[π1,π2]b[ξ1,ξ2]u+a[π1,π2]2q+b[ξ1,ξ2]u[π1,π2]q−c[π1,π2]2=0, | (3.20) |
for all π1,π2,ξ1,ξ2∈R. Specifically, Qr satisfies:
p[π1,π2]b[ξ1,ξ2]u+b[ξ1,ξ2]u[π1,π2]q=0, | (3.21) |
for all π1,π2,ξ1,ξ2∈R. Setting ξ1=π1 and ξ2=π2 in Eq (3.21), we get:
p[π1,π2]b[π1,π2]u+b[π1,π2]u[π1,π2]q=0, | (3.22) |
for all π1,π2∈R. Now, by Lemma (3.4), either b∈C or u∈C.
Sub-case 1: First, we assume that b∈C. Then, Eq (3.22) reduces to:
p[π1,π2]2bu+[π1,π2]bu[π1,π2]q=0, | (3.23) |
for all π1,π2∈R. Similarly, by parallel arguments, we obtain bu∈C or q,buq∈C.
Assume that bu∈C. If bu≠0, then from Eq (3.23), we have:
p[π1,π2]2+[π1,π2]2q=0, | (3.24) |
for all π1,π2∈R. Then, by Lemma 3.6, R either satisfies s4, which is our conclusion, or p+q=0, a contradiction.
If bu=0, then either b=0 or u=0. If b=0, we conclude as before. Assuming u=0, then from Eq (3.20), Qr satisfies:
p[π1,π2]a[π1,π2]+a[π1,π2]2q−c[π1,π2]2=0, | (3.25) |
for all π1,π2∈R. Eq (3.25) is analogous to Eq (3.14), and thus, we reach the required conclusion by previous arguments.
Now, assume that q,buq∈C. If q≠0, then bu∈C. Thus, the conclusion follows from the previous argument. If q=0, then from Eq (3.23), we get p[π1,π2]2bu=0. It follows from [25] that either p=0 or bu=0. If p=0, then p+q=0∈C, a contradiction. Again, if bu=0, then we get our conclusion from previous arguments.
Suba-case 2: If u∈C, then we get our conclusion by previous arguments.
In this final section, we aim to prove the main result, Theorem 1.6. Throughout the proof, we assume that R does not satisfies s4. According to [26], there exist elements a,c∈Qr and skew derivations d and g associated with the automorphism α, such that Δ1(π)=aπ+bd(π) and Δ2(x)=cπ+bg(π) for all π∈R. Given that L is noncentral and the characteristic of R is not 2, there is a nonzero ideal J of R such that 0≠[J,R]⊆L (see [27], p.45; [28], Lemma 2 and Proposition 1; [29], Theorem 4). Consequently, we have:
pΠΔ1(Π)+Δ1(Π)Πq=Δ2(Π2),for all Π∈[J,J]. |
Since R, Qr, and J satisfy the same generalized differential identities, the following holds for all X∈[R,R]:
pΠΔ1(Π)+Δ(Π)Πq=Δ2(Π2). |
Thus, Qr satisfies:
p[π,ξ]a[π,ξ]+p[π,ξ]bd([π,ξ])+a[π,ξ]2q+bd([π,ξ])[π,ξ]q−c[π,ξ]2−bg([π,ξ]2)=0, | (4.1) |
for all π,ξ∈R.
dis a skew inner derivation andgis a skew outer derivation. |
Since d is a skew inner derivation of R, there exists an element b′∈Qr such that d(π)=b′π−α(π)b′ for all π∈R. Substituting this into Eq (4.1), we obtain:
p[π,ξ]a[π,ξ]+p[π,ξ]b(b′[π,ξ]−α([π,ξ])b′)+a[π,ξ]2q+b(b′[π,ξ]−α([π,ξ])b′)[π,ξ]q−c[π,ξ]2−bg([π,ξ]2)=0 | (4.2) |
for all π,ξ∈R.
Applying the definition of g, we have:
p[π,ξ]a[π,ξ]−c[π,ξ]2+p[π,ξ]b(b′[π,ξ]−α([π,ξ])b′)+a[π,ξ]2q+b(b′[π,ξ]−α([π,ξ])b′)[π,ξ]q−b{(g(π)ξ+α(π)g(ξ)−g(ξ)π−α(ξ)g(π))[π,ξ]+α([π,ξ])(g(π)ξ+α(π)g(ξ)−g(ξ)π−α(ξ)g(π))}=0, | (4.3) |
for all π,ξ∈R. Since g is a skew outer derivation, applying Chuang's theorem (see Fact 2.4) to Eq (4.3), we obtain:
p[π,ξ]a[π,ξ]+p[π,ξ]b(b′[π,ξ]−α([π,ξ])b′)+a[π,ξ]2q+b(b′[π,ξ]−α([π,ξ])b′)[π,ξ]q−c[π,ξ]2−b{(s1ξ+α(π)s2−s2π−α(ξ)s1)[π,ξ]+α([π,ξ])(s1ξ+α(π)s2−s2π−α(ξ)s1)}=0, | (4.4) |
for all π,ξ,s1,s2∈R. Specifically, setting s2=0 in Eq (4.4), we obtain:
b{(α(π)s2−s2π)[π,ξ]+α([π,ξ])(α(π)s2−s2π)}=0, | (4.5) |
for all π,ξ,s2∈R.
Now, if the automorphism α is not inner, then according to [30], Eq (4.5) simplifies to:
b{(s3s2−s2π)[π,ξ]+[s3,s4](s3s2−s2π)}=0, |
for all π,ξ,s3,s2∈R. In particular, we have:
2b[π,ξ]2=0⟹b=0, |
which implies that both Δ1 and Δ2 are inner b-generalized skew derivations, contradicting our initial assumption.
Furthermore, if α is an inner automorphism, then there exists some t∈Qr such that α(π)=tπt−1, and Eq (4.5) simplifies to:
b{(tπt−1s2−s2π)[π,ξ]+t[π,ξ]t−1(tπt−1s2−s2π)}=0, | (4.6) |
for all π,ξ,s2∈R.
In particular, setting s2=tξ, we get:
2bt[π,ξ]2=0⟹bt=0⇒b=0, |
which again leads to a contradiction.
dis skew outer andgis skew inner derivation. |
In this scenario, there exists an element c∈Qr such that g(π)=cπ−α(π)c for all π∈R. Consequently, Eq (4.1) simplifies to:
p[π,ξ]a[π,ξ]+p[π,ξ]b(d(π)ξ+α(π)d(ξ)−d(ξ)π−α(ξ)d(π))+a[π,ξ]2q+b(d(π)ξ+α(π)d(ξ)−d(ξ)π−α(ξ)d(π))[π,ξ]q−c[π,ξ]2−b(c[π,ξ]2−α([π,ξ]2)c)=0 | (4.7) |
for all π,ξ∈R. Since d is an outer derivation, by applying Fact 2.4, Eq (4.7) further reduces to:
p[π,ξ]a[π,ξ]+p[π,ξ]b(s1ξ+α(π)s2−s2π−α(ξ)s1)−c[π,ξ]2−b(c[π,ξ]2+a[π,ξ]2q+b(s1ξ+α(π)s2−s2π−α(ξ)s1)[π,ξ]q)−α([π,ξ]2)c=0 | (4.8) |
for all π,ξ,s1,s2∈R. In particular, setting s2=0 in Eq (4.8), Qr satisfies the following:
p[π,ξ]b(α(π)s2−s2π)+b(α(π)s2−s2π)[π,ξ]q=0 | (4.9) |
for all π,ξ,s2∈R.
Now, if the automorphism α is not inner, then according to [30], Eq (4.9) simplifies to:
p[π,ξ]b(z1s2−s2π)+b(z1s2−s2π)[π,ξ]q=0 |
for all π,ξ,z1,s2∈R. Specifically, we have:
p[π,ξ]b[π,ξ]+b[π,ξ]2q=0 | (4.10) |
for all π,ξ∈R. By Lemma 3.5, it follows that either p,pb∈C, or b∈C.
First, assume that p,pb∈C. If p=0, then from Eq (4.10), we get q=0, implying p+q=0, which is a contradiction. If p≠0, then b∈C, and thus, from Eq (4.10) we obtain [π,ξ]2(p+q)=0, implying p+q=0, which is also a contradiction.
Next, if b∈C, similar arguments lead to the conclusion that p+q=0, again resulting in a contradiction.
Moreover, if the automorphism α is inner, there exists an element t∈Qr such that α(π)=tπt−1. In this case, Eq (4.9) reduces to:
p[π,ξ]b(tπt−1s2−s2π)+b(tπt−1s2−s2π)[π,ξ]q=0 | (4.11) |
for all π,ξ,s2∈R. Substituting s2=tξ into Eq (4.11), we obtain:
p[π,ξ]bt[π,ξ]+bt[π,ξ]2q=0 | (4.12) |
for all π,ξ∈R. Since Eq (4.12) is analogous to Eq (4.10), the previous arguments lead us to the same contradiction.
Now, let's consider the case where both d and g are skew outer derivations. Then, we have the following scenarios:
dandgareC-modulo independent. |
In this case, after applying the definitions of d and g, Qr satisfies the following equation:
p[π,ξ]a[π,ξ]+p[π,ξ]b(d(π)ξ+α(π)d(ξ)−d(ξ)π−α(ξ)d(π))+a[π,ξ]2q+b(d(π)ξ+α(π)d(ξ)−d(ξ)π−α(ξ)d(π))[π,ξ]q−c[π,ξ]2−b{(g(π)ξ+α(π)g(ξ)−g(ξ)π−α(ξ)g(π))[π,ξ]+α([π,ξ])(g(π)ξ+α(π)g(ξ)−g(ξ)π−α(ξ)g(π))}=0 | (4.13) |
for all π,ξ∈R. Then, by Chuang's theorem (see Fact 2.4), Eq (4.13) reduces to
p[π,ξ]a[π,ξ]+p[π,ξ]b(s1ξ+α(π)s2−s2π−α(ξ)s1)+a[π,ξ]2q−c[π,ξ]2+b(s1ξ+α(π)s2−s2π−α(ξ)s1)[π,ξ]q−b{(s3ξ+α(π)s4−s4π−α(ξ)s3)[π,ξ]+α([π,ξ])(s3ξ+α(π)s4−s4π−α(ξ)s3)}=0 | (4.14) |
for all π,ξ,s1,s2,s3,s4∈R. Choosing s4=0 in Eq (4.14), we obtain:
b{(α(π)s2−s2π)[π,ξ]+α([π,ξ])(α(π)s2−s2π)}=0 | (4.15) |
for all s2,π,ξ∈R. Now, Eq (4.15) is analogous to Eq (4.5). Therefore, using similar arguments as in Case 1, we arrive at a contradiction.
dandgareC-modulo dependent. |
Consider the case where Δ1(π)=aπ+bd(π) and Δ2(π)=cπ+bg(π) for all π∈R, where a,c∈Qr are suitable constants, and d and g are nonzero skew derivations of R associated with the automorphism α. Additionally, assume that d and g are linearly C-dependent modulo inner skew derivations. Then, there exist η,τ∈C, v∈Qr, and an automorphism ϕ of R such that
ηd(π)+τg(π)=vπ−ϕ(π)v,for all π∈R. |
Case 1: η≠0 and τ≠0. Then, we have
d(π)=a1g(π)+(a2π−ϕ(π)a2),for all π∈R,where a1=−η−1τ,a2=η−1v. |
It is important to note that if d is an inner skew derivation, then, according to Fact 2.9, g also becomes an inner skew derivation. In this case, the conclusion follows directly from Proposition 3.7. Therefore, in the following analysis, we will assume that d is a nonzero outer skew derivation. Consequently, using Fact 2.8, we conclude that either ϕ=α or a2=0. Summarizing, we reach one of the following conclusions:
a) d(π)=a1g(π)+(a2π−α(π)a2),
b) d(π)=a1g(π).
We now demonstrate that each of these conditions leads to a contradiction. For brevity, we focus on Case 1, as it can be shown that Case 2 follows from Case 1. Thus, let d(π)=a1g(π)+(a2π−α(π)a2) for all π∈R.
Thus, from Eq (4.1), we have
p[π,ξ]a[π,ξ]+p[π,ξ]b(a1g([π,ξ])+(a2[π,ξ]−α([π,ξ])a2))−c[π,ξ]2+a[π,ξ]2q+b(a1g([π,ξ])+(a2[π,ξ]−α([π,ξ])a2))[π,ξ]q−bg([π,ξ])2=0 | (4.16) |
for all π,ξ∈R. Using the definition of g in Eq (4.16), we have
p[π,ξ]a[π,ξ]+a[π,ξ]2q+b{a1(g(π)ξ+α(π)g(ξ)−g(ξ)π−α(ξ)g(π))}[π,ξ]q+p[π,ξ]b{a1(g(π)ξ+α(π)g(ξ)−g(ξ)π−α(ξ)g(π))+(a2[π,ξ]−α([π,ξ])a2)}−b{(g(π)ξ+α(π)g(ξ)−g(ξ)π−α(ξ)g(π))[π,ξ]+α([π,ξ])(g(π)ξ+α(π)g(ξ)−g(ξ)π−α(ξ)g(π))}+b{a2[π,ξ]−α([π,ξ])a2}[π,ξ]q−c[π,ξ]2=0 | (4.17) |
for all π,ξ∈R. Applying Fact 2.4 in Eq (4.17), we obtain:
p[π,ξ]a[π,ξ]+a[π,ξ]2q−c[π,ξ]2+p[π,ξ]b{a1(s1ξ+α(π)s2−s2π−α(ξ)s1)+(a2[π,ξ]−α([π,ξ])a2)}−b{(s1ξ+α(π)s2−s2π−α(ξ)s1)[π,ξ]+α([π,ξ])(s1ξ+α(π)s2−s2π−α(ξ)s1)}+b{a1(s1ξ+α(π)s2−s2π−α(ξ)s1)+(a2[π,ξ]−α([π,ξ])a2)}[π,ξ]q=0 | (4.18) |
for all π,ξ,s1,s2∈R. Now, choosing s1=0 in Eq (4.18), we obtain:
p[π,ξ]b(a1(s1ξ−α(ξ)s1))+b(a1(s1ξ−α(ξ)s1))[π,ξ]q−b{(s1ξ−α(ξ)s1)[π,ξ]+[α(π),α(ξ)](s1ξ−α(ξ)s1)}=0 | (4.19) |
for all π,ξ,s1∈R. If the automorphism α is not inner, then from [15]
p[π,ξ]b(a1(s1ξ−s3s1))+b(a1(s1ξ−s3s1))[π,ξ]q−b{(s1ξ−s3s1)[π,ξ]+[s1,s3](s1ξ−s3s1)}=0 | (4.20) |
for all π,ξ,s1,s3∈R. In particular, choosing π=0 and ξ=s3 in Eq (4.20), we have
b[s1,s3]2=0 | (4.21) |
for all s1,s3∈R, which implies that b=0, a contradiction. Suppose the automorphism α is inner, then there exists t∈Qr such that α(π)=tπt−1, and Eq (4.19) takes the form
p[π,ξ]b(a1(s1ξ−tξt−1s1))+b(a1(s1ξ−tξt−1s1))[π,ξ]q−b{(s1ξ−tξt−1s1)[π,ξ]+[tπt−1,tξt−1](s1ξ−tξt−1s1)}=0 | (4.22) |
for all π,ξ,s1∈R. In particular, choosing s1=ts1 and π=tπ in Eq (4.22), we obtain:
p[π,ξ]bt(a1(s1ξ−ξs1))+b(a1t(s1ξ−ξs1))[π,ξ]q−bt{(s1ξ−ξs1)[π,ξ]+[π,ξ](s1ξ−ξs1)}=0 | (4.23) |
for all π,ξ,s1∈R. In particular, we get:
p[π1,π2]bta1[π1,π2]+bta1[π1,π2]2q−2bt[π1,π2]2=0 | (4.24) |
for all π1,π2∈R. Thus, from Lemma (3.4), we get p+q∈C, a contradiction.
Case 2: η=0 and τ≠0. Then, we have
g(π)=a2π−ϕ(π)a2, for all π∈R, where a2=τ−1v. |
In this case, we can assume that the skew derivation d is not inner. If it were inner, the conclusion would follow from Proposition 3.7. Additionally, since the automorphism associated with a skew derivation is unique, in this scenario, we have ϕ=α. Therefore, Qr satisfies:
p[π,ξ]a[π,ξ]+p[π,ξ]bd([π,ξ])+a[π,ξ]2q+bd([π,ξ])[π,ξ]q−c[π,ξ]2−b(a2[π,ξ])2−α([π,ξ]2a2))=0 | (4.25) |
for all π,ξ∈R. Applying the definition of d in Eq (4.25), we get
p[π,ξ]a[π,ξ]+p[π,ξ]b{d(π)ξ+α(π)d(ξ)−d(ξ)π−α(ξ)d(π)}+a[π,ξ]2q−c[π,ξ]2+b{d(π)ξ+α(π)d(ξ)−d(ξ)π−α(ξ)d(π)}[π,ξ]q−b(a2[π,ξ])2−α([π,ξ]2a2))=0 | (4.26) |
for all π,ξ∈R. Then by using Fact 2.4 in Eq (4.26), we obtain:
p[π,ξ]a[π,ξ]+p[π,ξ]b(s2ξ+α(π)s1−s1π−α(ξ)s2)+a[π,ξ]2q−c[π,ξ]2+b(s2ξ+α(π)s1−s1π−α(ξ)s2)[π,ξ]q−b(a2[π,ξ])2−α([π,ξ]2a2))=0 | (4.27) |
for all π,ξ,s2,s1∈R. In particular, choosing s1=0 in Eq (4.27), Qr satisfies the blended component
p[π,ξ]b(α(π)s1−s1π)+b(α(π)s1−s1π)[π,ξ]q=0 | (4.28) |
for all π,ξ,s1∈R. The above Eq (4.28) is similar to Eq (4.9), therefore this case also leads to a contradiction.
Case 3: η≠0 and τ=0. Then, we have
d(π)=a1π−ϕ(π)a1, for all π∈R, where a1=η−1v. |
Similar to Case 2, here we are assuming that g is not inner and α=ϕ. Hence, Qr satisfies:
p[π,ξ]a[π,ξ]+p[π,ξ]b(a1[π,ξ]−α([π,ξ]a1))+a[π,ξ]2q+b(a1[π,ξ]−b{(g(π)ξ+α(π)g(ξ)−g(ξ)π−α(ξ)g(π))[π,ξ]+α([π,ξ])(g(π)ξ+α(π)g(ξ)−g(ξ)π−α(ξ)g(π))}−α([π,ξ]a1))[π,ξ]q−c[π,ξ]2=0 | (4.29) |
for all π,ξ∈R. Then, from Fact 2.4, we have
p[π,ξ]a[π,ξ]+p[π,ξ]b(a1[π,ξ]−α([π,ξ]a1))−α([π,ξ]a1))[π,ξ]q−c[π,ξ]2−b{(s2ξ+α(π)s1−s1π−α(ξ)s2)[π,ξ]+α([π,ξ])(s2ξ+α(π)s1−s1π−α(ξ)s2)}+a[π,ξ]2q+b(a1[π,ξ])=0 | (4.30) |
for all π,ξ,s2,s1∈R. Choosing s1=0 in Eq (4.30), we have
b{α(π)s1−s1π)[π,ξ]+α([π,ξ])(α(π)s1−s1π)}=0 | (4.31) |
for all π,ξ,s1∈R. The above (4.31) is similar to Eq (4.5), therefore, this case also leads to a contradiction.
In this article, we characterize all possible forms of b-generalized skew derivations Δ1 and Δ2 that satisfy the identity pπΔ1(π)+Δ1(π)πq=Δ2(π2) for all π∈L. The advantage of the methodology used in this article is that it can be applied to all additive maps for which Facts 2.3 and 2.4 hold. Unfortunately, however, it does not hold for many additive maps for example, it is not valid for (α,β)-derivations [31].
It would be an interesting problem to study this identity further by considering the case p+q∈C or by examining the identity pπΔ1(π)+Δ1(π)Δ3(π)=Δ2(π2), for all π∈L, where Δ3 is another b-generalized skew derivation.
Ashutosh Pandey: contributed to the conceptualization and formulation of the problem, conducted a significant portion of the mathematical analysis, and participated in drafting the manuscript; Mani Shankar Pandey: played a central role in developing the theoretical framework, performing key computations, and revising the manuscript; Omaima Alshanqiti: provided guidance on the research methodology, assisted with the mathematical proofs, and was involved in reviewing and refining the manuscript. All authors read and approved the final version of the manuscript.
We would like to extend our sincere gratitude to the referees for their insightful comments and suggestions, which have greatly contributed to improving the quality of this paper.
The authors declare that they have no conflicts of interest or competing interests that could have influenced the results and/or discussion presented in this paper.
[1] |
M. N. Ali, K. Mahmoud, M. Lehtonen, M. M. F. Darwish, Promising MPPT methods combining metaheuristic, fuzzy-logic and ANN techniques for grid-connected photovoltaic, Sensors, 21 (2021), 1244. https://doi.org/10.3390/s21041244 doi: 10.3390/s21041244
![]() |
[2] |
D. S. AbdElminaam, E. H. Houssein, M. Said, D. Oliva, A. Nabil, An efficient heap-based optimizer for parameters identification of modified photovoltaic models, Ain Shams Eng. J., 13 (2022), 101728. https://doi.org/10.1016/j.asej.2022.101728 doi: 10.1016/j.asej.2022.101728
![]() |
[3] |
A. A. K. Ismaeel, E. H. Houssein, D. Oliva, M. Said, Gradient-based optimizer for parameter extraction in photovoltaic models, IEEE Access, 9 (2021), 13403–13416. https://doi.org/10.1109/ACCESS.2021.3052153 doi: 10.1109/ACCESS.2021.3052153
![]() |
[4] |
E. H. Houssein, S. Deb, D. Oliva, H. Rezk, H. Alhumade, M. Said, Performance of gradient-based optimizer on charging station placement problem, Mathematics, 9 (2021), 2821. https://doi.org/10.3390/math9212821 doi: 10.3390/math9212821
![]() |
[5] |
D. S. Abdelminaam, M. Said, E. H. Houssein, Turbulent flow of water-based optimization using new objective function for parameter extraction of six photovoltaic models., IEEE Access, 9 (2021), 35382–35398. https://doi.org/10.1109/ACCESS.2021.3061529 doi: 10.1109/ACCESS.2021.3061529
![]() |
[6] |
M. Said, E. H. Houssein, S. Deb, A. A. Alhussan, R. M. Ghoniem, A novel gradient-based optimizer for solving unit commitment problem, IEEE Access, 10 (2022), 18081–18092. https://doi.org/10.1109/ACCESS.2022.3150857 doi: 10.1109/ACCESS.2022.3150857
![]() |
[7] |
E. H. Houssein, D. Oliva, N. A. Samee, N. F. Mahmoud, M. M. Emam, Liver cancer algorithm: A novel bio-inspired optimizer, Comput. Biol. Med., 165 (2023), 107389. https://doi.org/10.1016/j.compbiomed.2023.107389 doi: 10.1016/j.compbiomed.2023.107389
![]() |
[8] |
S. Li, H. Chen, M. Wang, A. A. Heidari, S. Mirjalili, Slime mould algorithm: A new method for stochastic optimization, Future Gener. Comp. Syst., 111 (2020), 300–323. https://doi.org/10.1016/j.future.2020.03.055 doi: 10.1016/j.future.2020.03.055
![]() |
[9] |
Y. Yang, H. Chena, A. A. Heidari, A. H. Gandomi, Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts, Expert Syst. Appl., 177 (2021), 114864. https://doi.org/10.1016/j.eswa.2021.114864 doi: 10.1016/j.eswa.2021.114864
![]() |
[10] |
I. Ahmadianfar, A. A. Heidari, A. H. Gandomi, X. Chu, H. Chen, RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method, Expert Syst. Appl., 181 (2021), 115079. https://doi.org/10.1016/j.eswa.2021.115079 doi: 10.1016/j.eswa.2021.115079
![]() |
[11] |
I. Ahmadianfar, A. A. Heidari, S. Noshadian, H. Chen, A. H. Gandomi, INFO: An efficient optimization algorithm based on weighted mean of vectors, Expert Syst. Appl., 195 (2022), 116516. https://doi.org/10.1016/j.eswa.2022.116516 doi: 10.1016/j.eswa.2022.116516
![]() |
[12] |
X. Yuan, Y. Liu, R. Bucknall, A novel design of a solid oxide fuel cell-based combined cooling, heat and power residential system in the U. K., IEEE T. Ind. Appl., 57 (2021), 805–813. https://doi.org/10.1109/TIA.2020.3034073 doi: 10.1109/TIA.2020.3034073
![]() |
[13] |
J. Ihonen, P. Koski, V. Pulkkinen, T. Keränen, H. Karimäki, S. Auvinen, et al., Operational experiences of PEMFC pilot plant using low grade hydrogen from sodium chlorate production process. Int. J. Hydrogen Energ., 42 (2017), 27269–27283. https://doi.org/10.1016/j.ijhydene.2017.09.056 doi: 10.1016/j.ijhydene.2017.09.056
![]() |
[14] |
Y. Qiu, P. Wu, T. Miao, J. Liang, K. Jiao, T. Li, et al., An intelligent approach for contact pressure optimization of PEM fuel cell gas diffusion layers, Appl. Sci., 10 (2020), 4194. https://doi.org/10.3390/app10124194 doi: 10.3390/app10124194
![]() |
[15] |
K. Ahmed, O. Farrok, M. M. Rahman, M. S. Ali, M. M. Haque, A. K. Azad, Proton exchange membrane hydrogen fuel cell as the grid connected power generator, Energies, 13 (2020), 6679. https://doi.org/10.3390/en13246679 doi: 10.3390/en13246679
![]() |
[16] |
K. Nikiforow, J. Pennanen, J. Ihonen, S. Uski, P. Koski, Power ramp rate capabilities of a 5 kW proton exchange membrane fuel cell system with discrete ejector control. J. Power Sources, 381 (2018), 30–37. https://doi.org/10.1016/j.jpowsour.2018.01.090 doi: 10.1016/j.jpowsour.2018.01.090
![]() |
[17] |
A. S. Menesy, H. M. Sultan, A. Korashy, F. A. Banakhr, M. G. Ashmawy, S. Kamel, Effective parameter extraction of different polymer electrolyte membrane fuel cell stack models using a modified artificial ecosystem optimization algorithm, IEEE Access, 8 (2020), 31892–31909. https://doi.org/10.1109/ACCESS.2020.2973351 doi: 10.1109/ACCESS.2020.2973351
![]() |
[18] | B. Sundén, Fuel cell types—Overview. In: Hydrogen, batteries and fuel cells, Cambridge, MA, USA: Academic Press, 2019,123–144. https://doi.org/10.1016/B978-0-12-816950-6.00008-7 |
[19] |
A. Fathy, H. Rezk, Multi-verse optimizer for identifying the optimal parameters of PEMFC model, Energy, 143 (2018), 634–644. https://doi.org/10.1016/j.energy.2017.11.014 doi: 10.1016/j.energy.2017.11.014
![]() |
[20] |
H. Ashraf, S. O. Abdellatif, M. M. Elkholy, A. A. El-Fergany, Computational techniques based on artificial intelligence for extracting optimal parameters of PEMFCs: Survey and insights, Arch. Comput. Methods Eng., 29 (2022), 3943–3972. https://doi.org/10.1007/s11831-022-09721-y doi: 10.1007/s11831-022-09721-y
![]() |
[21] |
H. Rezk, A. G. Olabi, E. Sayed, T. Wilberforce, Role of metaheuristics in optimizing microgrids operating and management issues: A comprehensive review, Sustainability, 15 (2023), 4982. https://doi.org/10.3390/su15064982 doi: 10.3390/su15064982
![]() |
[22] |
Y. Zhu, N. Yousefi, Optimal parameter identification of PEMFC stacks using adaptive sparrow search algorithm, Int. J. Hydrogen Energ., 46 (2021), 9541–9552. https://doi.org/10.1016/j.ijhydene.2020.12.107 doi: 10.1016/j.ijhydene.2020.12.107
![]() |
[23] |
D. Yousri, S. Mirjalili, J. A. T. Machado, S. B. Thanikanti, O. Elbaksawi, A. Fathy, Efficient fractional-order modified Harris hawks optimizer for proton exchange membrane fuel cell modeling, Eng. Appl. Artif. Intel., 100 (2021), 104193. https://doi.org/10.1016/j.engappai.2021.104193 doi: 10.1016/j.engappai.2021.104193
![]() |
[24] |
Z. Yuan, W. Wang, H. Wang, A. Yildizbasi, Developed coyote optimization algorithm and its application to optimal parameters estimation of PEMFC model, Energy Rep., 6 (2020), 1106–1117. https://doi.org/10.1016/j.egyr.2020.04.032 doi: 10.1016/j.egyr.2020.04.032
![]() |
[25] |
S. Bao, A. Ebadi, M. Toughani, J. Dalle, A. Maseleno, Baharuddin, et al., A new method for optimal parameters identification of a PEMFC using an improved version of monarch butterfly optimization algorithm, Int. J. Hydrogen Energ., 45 (2020), 17882–17892. https://doi.org/10.1016/j.ijhydene.2020.04.256 doi: 10.1016/j.ijhydene.2020.04.256
![]() |
[26] |
T. Wilberforce, H. Rezk, A. G. Olabi, E. I. Epelle, M. A. Abdelkareem, Comparative analysis on parametric estimation of a PEM fuel cell using metaheuristics algorithms, Energy, 262 (2023), 125530. https://doi.org/10.1016/j.energy.2022.125530 doi: 10.1016/j.energy.2022.125530
![]() |
[27] |
A. Fathy, M. A. Elaziz, A. G. Alharbi, A novel approach based on hybrid vortex search algorithm and differential evolution for identifying the optimal parameters of PEM fuel cell, Renew. Energ., 146 (2020), 1833–1845. https://doi.org/10.1016/j.renene.2019.08.046 doi: 10.1016/j.renene.2019.08.046
![]() |
[28] |
Z. Yuan, W. Wang, H. Wang, Optimal parameter estimation for PEMFC using modified monarch butterfly optimization, Int. J. Energ. Res., 44 (2020), 8427–8441. https://doi.org/10.1002/er.5527 doi: 10.1002/er.5527
![]() |
[29] |
Z. Yuan, W. Wang, H. Wang, N. Razmjooy, A new technique for optimal estimation of the circuit-based PEMFCs using developed Sunflower Optimization Algorithm, Energy Rep., 6 (2020), 662–671. https://doi.org/10.1016/j.egyr.2020.03.010 doi: 10.1016/j.egyr.2020.03.010
![]() |
[30] |
S. Sun, Y. Su, C. Yin, K. Jermsittiparsert, Optimal parameters estimation of PEMFCs model using converged moth search algorithm, Energy Rep., 6 (2020), 1501–1509. https://doi.org/10.1016/j.egyr.2020.06.002 doi: 10.1016/j.egyr.2020.06.002
![]() |
[31] |
R. Syah, L. A. Isola, J. W. G. Guerrero, W. Suksatan, D. Sunarsi, M. Elveny, et al., Optimal parameters estimation of the PEMFC using a balanced version of Water Strider Algorithm, Energy Rep., 7 (2021), 6876–6886. https://doi.org/10.1016/j.egyr.2021.10.057 doi: 10.1016/j.egyr.2021.10.057
![]() |
[32] |
H. Guo, H. Tao, S. Q. Salih, Z. M. Yaseen, Optimized parameter estimation of a PEMFC model based on improved Grass Fibrous Root Optimization Algorithm, Energy Rep., 6 (2020), 1510–1519. https://doi.org/10.1016/j.egyr.2020.06.001 doi: 10.1016/j.egyr.2020.06.001
![]() |
[33] |
M. A. Mossa, O. M. Kamel, H. M. Sultan, A. A. Z. Diab, Parameter estimation of PEMFC model based on Harris Hawks' optimization and atom search optimization algorithms, Neural Comput. Appl., 33 (2021), 5555–5570. https://doi.org/10.1007/s00521-020-05333-4 doi: 10.1007/s00521-020-05333-4
![]() |
[34] |
H. Rezk, S. Ferahtia, A. Djeroui, A. Chouder, A. Houari, M. Machmoum, et al., Optimal parameter estimation strategy of PEM fuel cell using gradient-based optimizer, Energy, 239 (2022), 122096. https://doi.org/10.1016/j.energy.2021.122096 doi: 10.1016/j.energy.2021.122096
![]() |
[35] |
G. Zhang, C. Xiao, N. Razmjooy, Optimal parameter extraction of PEM fuel cells by meta-heuristics, Int. J. Ambient Energy, 43 (2020), 2510–2519. https://doi.org/10.1080/01430750.2020.1745276 doi: 10.1080/01430750.2020.1745276
![]() |
[36] |
W. Han, D. Li, D. Yu, H. Ebrahimian, Optimal parameters of PEM fuel cells using chaotic binary shark smell optimizer, Energy Sources Part A, 45 (2019), 7770–7784. https://doi.org/10.1080/15567036.2019.1676845 doi: 10.1080/15567036.2019.1676845
![]() |
[37] |
A. Fathy, T. S. Babu, M. A. Abdelkareem, H. Rezk, D. Yousri, Recent approach based heterogeneous comprehensive learning archimedes optimization algorithm for identifying the optimal parameters of different fuel cells, Energy, 248 (2022), 123587. https://doi.org/10.1016/j.energy.2022.123587 doi: 10.1016/j.energy.2022.123587
![]() |
[38] |
L. Blanco-Cocom, S. Botello-Rionda, L. Ordoñez, S. I. Valdez, Robust parameter estimation of a PEMFC via optimization based on probabilistic model building, Math. Comput. Simulat., 185 (2021), 218–237. https://doi.org/10.1016/j.matcom.2020.12.021 doi: 10.1016/j.matcom.2020.12.021
![]() |
[39] |
X. Lu, D. Kanghong, L. Guo, P. Wang, A. Yildizbasi, Optimal estimation of the proton exchange membrane fuel cell model parameters based on extended version of crow search algorithm, J. Clean. Prod., 272 (2020), 122640. https://doi.org/10.1016/j.jclepro.2020.122640 doi: 10.1016/j.jclepro.2020.122640
![]() |
[40] | A. S. Menesy, H. M. Sultan, S. Kamel, Extracting model parameters of proton exchange membrane fuel cell using equilibrium optimizer algorithm, In: 2020 International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE), 2020. https://doi.org/10.1109/REEPE49198.2020.9059219 |
[41] |
B. Duan, Q. Cao, N. Afshar, Optimal parameter identification for the proton exchange membrane fuel cell using satin bowerbird optimizer, Int. J. Energ. Res., 43 (2019), 8623–8632. https://doi.org/10.1002/er.4859 doi: 10.1002/er.4859
![]() |
[42] |
A. Fathy, S. H. E. A. Aleem, H. Rezk, A novel approach for PEM fuel cell parameter estimation using LSHADE-EpSin optimization algorithm, Int. J. Energ. Res., 45 (2021), 6922–6942. https://doi.org/10.1002/er.6282 doi: 10.1002/er.6282
![]() |
[43] | Z. M. Isa, N. M. Nayan, M. H. Arshad, N. A. M. Kajaan, Optimizing PEMFC model parameters using ant lion optimizer and dragonfly algorithm: A comparative study, Int. J. Electr. Comput. Eng., 9 (2019), 5312–5320. http://dx.doi.org/10.11591/ijece.v9i6.pp5295-5303 |
[44] |
Y. Song, X. Tan, S. Mizzi, Optimal parameter extraction of the proton exchange membrane fuel cells based on a new Harris Hawks optimization algorithm, Energy Sources Part A, 2020, 1–18. https://doi.org/10.1080/15567036.2020.1769230 doi: 10.1080/15567036.2020.1769230
![]() |
[45] |
Z. Yang, Q. Liu, L. Zhang, J. Dai, N. Razmjooy, Model parameter estimation of the PEMFCs using improved Barnacles Mating Optimization Algorithm, Energy, 212 (2020), 118738. https://doi.org/10.1016/j.energy.2020.118738 doi: 10.1016/j.energy.2020.118738
![]() |
[46] |
X. Sun, G. Wang, L. Xu, H. Yuan, N. Yousefi, Optimal estimation of the PEM fuel cells applying deep belief network optimized by improved Archimedes optimization algorithm, Energy, 237 (2021), 121532. https://doi.org/10.1016/j.energy.2021.121532 doi: 10.1016/j.energy.2021.121532
![]() |
[47] |
H. M. Hasanien, M. A. M. Shaheen, R. A. Turky, M. H. Qais, S. Alghuwainem, S. Kamel, et al., Precise modeling of PEM fuel cell using a novel enhanced transient search optimization algorithm, Energy, 247 (2022), 123530. https://doi.org/10.1016/j.energy.2022.123530 doi: 10.1016/j.energy.2022.123530
![]() |
[48] |
M. Calasan, S. H. E. A. Aleem, H. M. Hasanien, Z. M. Alaas, Z. M. Ali, An innovative approach for mathematical modeling and parameter estimation of PEM fuel cells based on iterative Lambert W function, Energy, 264 (2023), 126165. https://doi.org/10.1016/j.energy.2022.126165 doi: 10.1016/j.energy.2022.126165
![]() |
[49] |
T. Wilberforce, A. G. Olabi, H. Rezk, A. Y. Abdelaziz, M. A. Abdelkareem, E. T. Sayed, Boosting the output power of PEM fuel cells by identifying best-operating conditions, Energ. Convers. Manage., 270 (2022), 116205. https://doi.org/10.1016/j.enconman.2022.116205 doi: 10.1016/j.enconman.2022.116205
![]() |
[50] |
H. Rezk, T. Wilberforce, E. T. Sayed, A. N. M. Alahmadi, A. G. Olabi, Finding best operational conditions of PEM fuel cell using adaptive neuro-fuzzy inference system and metaheuristics, Energy Rep., 8 (2022), 6181–6190. https://doi.org/10.1016/j.egyr.2022.04.061 doi: 10.1016/j.egyr.2022.04.061
![]() |
[51] |
T. Wilberforce, A. G. Olabi, D. Monopoli, M. Dassisti, E. T. Sayed, M. A. Abdelkareem, Design optimization of proton exchange membrane fuel cell bipolar plate, Energ. Convers. Manage., 277 (2023), 116586. https://doi.org/10.1016/j.enconman.2022.116586 doi: 10.1016/j.enconman.2022.116586
![]() |
[52] |
H. Ashraf, S. O. Abdellatif, M. M. Elkholy, A. A. El-Fergany, Honey badger optimizer for extracting the ungiven parameters of PEMFC model: Steady-state assessment, Energ. Convers. Manage., 258 (2022), 115521. https://doi.org/10.1016/j.enconman.2022.115521 doi: 10.1016/j.enconman.2022.115521
![]() |
[53] |
S. K. Eelsayed, A. Agwa, E. E. Elattar, A. El-Fergany, Steady-state modelling of pem fuel cells using gradientbased optimizer, Dyna, 96 (2021), 520–527. http://doi.org/10.6036/10099 doi: 10.6036/10099
![]() |
[54] |
M. Han, Z. Du, K. F. Yuen, H. Zhu, Y. Li, Q. Yuan, Walrus optimizer: A novel nature-inspired metaheuristic algorithm, Expert Syst. Appl., 239 (2024), 122413. https://doi.org/10.1016/j.eswa.2023.122413 doi: 10.1016/j.eswa.2023.122413
![]() |
[55] |
S. Kaur, L. K. Awasthi, A. L. Sangal, G. Dhiman, Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization, Eng. Appl. Artif. Intel., 90 (2020), 103541. https://doi.org/10.1016/j.engappai.2020.103541 doi: 10.1016/j.engappai.2020.103541
![]() |
[56] |
A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, Harris hawks optimization: Algorithm and applications, Future Gener. Comput. Syst., 97 (2019), 849–872. https://doi.org/10.1016/j.future.2019.02.028 doi: 10.1016/j.future.2019.02.028
![]() |
[57] |
Q. Askari, M. Saeed, I. Younas, Heap-based optimizer inspired by corporate rank hierarchy for global optimization, Expert Syst. Appl., 161 (2020), 113702. https://doi.org/10.1016/j.eswa.2020.113702 doi: 10.1016/j.eswa.2020.113702
![]() |
[58] |
M. Khishe, M. R. Mosavi, Chimp optimization algorithm, Expert Syst. Appl., 149 (2020), 113338. https://doi.org/10.1016/j.eswa.2020.113338 doi: 10.1016/j.eswa.2020.113338
![]() |
[59] |
M. Dehghani, P. Trojovský, Osprey optimization algorithm: A new bioinspired metaheuristic algorithm for solving engineering optimization problems, Front. Mech. Eng., 8 (2023), 1126450. https://doi.org/10.3389/fmech.2022.1126450 doi: 10.3389/fmech.2022.1126450
![]() |
[60] |
S. I. Seleem, H. M. Hasanie, A. A. El-Fergany, Equilibrium optimizer for parameter extraction of a fuel cell dynamic model, Renew. Energ., 169 (2021), 117–128. https://doi.org/10.1016/j.renene.2020.12.131 doi: 10.1016/j.renene.2020.12.131
![]() |